{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the code for the Chapter 2 - clustering. We are using 3 algorithms: kmeans, hierarchical and DBSCAN. The same dataset is used for clustering. The data is uploaded to git repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "vehicle_df = pd.read_csv('vehicle.csv').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(813, 19)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 813 entries, 0 to 845\n",
      "Data columns (total 19 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   compactness                  813 non-null    int64  \n",
      " 1   circularity                  813 non-null    float64\n",
      " 2   distance_circularity         813 non-null    float64\n",
      " 3   radius_ratio                 813 non-null    float64\n",
      " 4   pr.axis_aspect_ratio         813 non-null    float64\n",
      " 5   max.length_aspect_ratio      813 non-null    int64  \n",
      " 6   scatter_ratio                813 non-null    float64\n",
      " 7   elongatedness                813 non-null    float64\n",
      " 8   pr.axis_rectangularity       813 non-null    float64\n",
      " 9   max.length_rectangularity    813 non-null    int64  \n",
      " 10  scaled_variance              813 non-null    float64\n",
      " 11  scaled_variance.1            813 non-null    float64\n",
      " 12  scaled_radius_of_gyration    813 non-null    float64\n",
      " 13  scaled_radius_of_gyration.1  813 non-null    float64\n",
      " 14  skewness_about               813 non-null    float64\n",
      " 15  skewness_about.1             813 non-null    float64\n",
      " 16  skewness_about.2             813 non-null    float64\n",
      " 17  hollows_ratio                813 non-null    int64  \n",
      " 18  class                        813 non-null    object \n",
      "dtypes: float64(14), int64(4), object(1)\n",
      "memory usage: 127.0+ KB\n"
     ]
    }
   ],
   "source": [
    "vehicle_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10</td>\n",
       "      <td>162.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>159</td>\n",
       "      <td>176.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>197</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>170.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>199</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>50.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10</td>\n",
       "      <td>207.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>158</td>\n",
       "      <td>223.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>196</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>160.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>207</td>\n",
       "      <td>van</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>44.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>52</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144</td>\n",
       "      <td>241.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>183</td>\n",
       "      <td>bus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0           95         48.0                  83.0         178.0   \n",
       "1           91         41.0                  84.0         141.0   \n",
       "2          104         50.0                 106.0         209.0   \n",
       "3           93         41.0                  82.0         159.0   \n",
       "4           85         44.0                  70.0         205.0   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0                  72.0                       10          162.0   \n",
       "1                  57.0                        9          149.0   \n",
       "2                  66.0                       10          207.0   \n",
       "3                  63.0                        9          144.0   \n",
       "4                 103.0                       52          149.0   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0           42.0                    20.0                        159   \n",
       "1           45.0                    19.0                        143   \n",
       "2           32.0                    23.0                        158   \n",
       "3           46.0                    19.0                        143   \n",
       "4           45.0                    19.0                        144   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0            176.0              379.0                      184.0   \n",
       "1            170.0              330.0                      158.0   \n",
       "2            223.0              635.0                      220.0   \n",
       "3            160.0              309.0                      127.0   \n",
       "4            241.0              325.0                      188.0   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                         70.0             6.0              16.0   \n",
       "1                         72.0             9.0              14.0   \n",
       "2                         73.0            14.0               9.0   \n",
       "3                         63.0             6.0              10.0   \n",
       "4                        127.0             9.0              11.0   \n",
       "\n",
       "   skewness_about.2  hollows_ratio class  \n",
       "0             187.0            197   van  \n",
       "1             189.0            199   van  \n",
       "2             188.0            196   car  \n",
       "3             199.0            207   van  \n",
       "4             180.0            183   bus  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car    413\n",
       "bus    205\n",
       "van    195\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Class is categorical, use value_counts function\n",
    "pd.value_counts(vehicle_df['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "pd.value_counts(vehicle_df[\"class\"]).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.value_counts(vehicle_df['class']).hist(bins=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "compactness                    0\n",
       "circularity                    0\n",
       "distance_circularity           0\n",
       "radius_ratio                   0\n",
       "pr.axis_aspect_ratio           0\n",
       "max.length_aspect_ratio        0\n",
       "scatter_ratio                  0\n",
       "elongatedness                  0\n",
       "pr.axis_rectangularity         0\n",
       "max.length_rectangularity      0\n",
       "scaled_variance                0\n",
       "scaled_variance.1              0\n",
       "scaled_radius_of_gyration      0\n",
       "scaled_radius_of_gyration.1    0\n",
       "skewness_about                 0\n",
       "skewness_about.1               0\n",
       "skewness_about.2               0\n",
       "hollows_ratio                  0\n",
       "class                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kmeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the dimensions of the data are not really known to us, it would be wise to standardize the data using z scores before we \n",
    "#go for any clustering methods. You can use zscore function to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle_df_1 = vehicle_df.drop('class', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "vehicle_df_1_z = vehicle_df_1.apply(zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.163231</td>\n",
       "      <td>0.520408</td>\n",
       "      <td>0.060669</td>\n",
       "      <td>0.264970</td>\n",
       "      <td>1.283254</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>-0.198517</td>\n",
       "      <td>0.129648</td>\n",
       "      <td>-0.217151</td>\n",
       "      <td>0.766312</td>\n",
       "      <td>-0.397397</td>\n",
       "      <td>-0.339014</td>\n",
       "      <td>0.301676</td>\n",
       "      <td>-0.321192</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>0.371287</td>\n",
       "      <td>-0.321809</td>\n",
       "      <td>0.171837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.322874</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>0.124067</td>\n",
       "      <td>-0.836393</td>\n",
       "      <td>-0.599253</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.590034</td>\n",
       "      <td>-0.618754</td>\n",
       "      <td>-0.502972</td>\n",
       "      <td>-0.053505</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>0.147109</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.442318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.256966</td>\n",
       "      <td>0.845988</td>\n",
       "      <td>1.518823</td>\n",
       "      <td>1.187734</td>\n",
       "      <td>0.530251</td>\n",
       "      <td>0.299721</td>\n",
       "      <td>1.162569</td>\n",
       "      <td>-1.152637</td>\n",
       "      <td>0.949438</td>\n",
       "      <td>0.697326</td>\n",
       "      <td>1.111591</td>\n",
       "      <td>1.122486</td>\n",
       "      <td>1.415804</td>\n",
       "      <td>0.080339</td>\n",
       "      <td>1.555006</td>\n",
       "      <td>-0.413338</td>\n",
       "      <td>-0.159204</td>\n",
       "      <td>0.036596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.079822</td>\n",
       "      <td>-0.619123</td>\n",
       "      <td>-0.002729</td>\n",
       "      <td>-0.300595</td>\n",
       "      <td>0.153750</td>\n",
       "      <td>0.085785</td>\n",
       "      <td>-0.742952</td>\n",
       "      <td>0.642562</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.337462</td>\n",
       "      <td>-0.911095</td>\n",
       "      <td>-0.738643</td>\n",
       "      <td>-1.462359</td>\n",
       "      <td>-1.258099</td>\n",
       "      <td>-0.071523</td>\n",
       "      <td>-0.301249</td>\n",
       "      <td>1.629444</td>\n",
       "      <td>1.524243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.052030</td>\n",
       "      <td>-0.130753</td>\n",
       "      <td>-0.763506</td>\n",
       "      <td>1.068668</td>\n",
       "      <td>5.173770</td>\n",
       "      <td>9.285029</td>\n",
       "      <td>-0.591720</td>\n",
       "      <td>0.514333</td>\n",
       "      <td>-0.606014</td>\n",
       "      <td>-0.268476</td>\n",
       "      <td>1.689501</td>\n",
       "      <td>-0.647299</td>\n",
       "      <td>0.425468</td>\n",
       "      <td>7.307905</td>\n",
       "      <td>0.538425</td>\n",
       "      <td>-0.189159</td>\n",
       "      <td>-1.460039</td>\n",
       "      <td>-1.721531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0     0.163231     0.520408              0.060669      0.264970   \n",
       "1    -0.322874    -0.619123              0.124067     -0.836393   \n",
       "2     1.256966     0.845988              1.518823      1.187734   \n",
       "3    -0.079822    -0.619123             -0.002729     -0.300595   \n",
       "4    -1.052030    -0.130753             -0.763506      1.068668   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0              1.283254                 0.299721      -0.198517   \n",
       "1             -0.599253                 0.085785      -0.591720   \n",
       "2              0.530251                 0.299721       1.162569   \n",
       "3              0.153750                 0.085785      -0.742952   \n",
       "4              5.173770                 9.285029      -0.591720   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0       0.129648               -0.217151                   0.766312   \n",
       "1       0.514333               -0.606014                  -0.337462   \n",
       "2      -1.152637                0.949438                   0.697326   \n",
       "3       0.642562               -0.606014                  -0.337462   \n",
       "4       0.514333               -0.606014                  -0.268476   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0        -0.397397          -0.339014                   0.301676   \n",
       "1        -0.590034          -0.618754                  -0.502972   \n",
       "2         1.111591           1.122486                   1.415804   \n",
       "3        -0.911095          -0.738643                  -1.462359   \n",
       "4         1.689501          -0.647299                   0.425468   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                    -0.321192       -0.071523          0.371287   \n",
       "1                    -0.053505        0.538425          0.147109   \n",
       "2                     0.080339        1.555006         -0.413338   \n",
       "3                    -1.258099       -0.071523         -0.301249   \n",
       "4                     7.307905        0.538425         -0.189159   \n",
       "\n",
       "   skewness_about.2  hollows_ratio  \n",
       "0         -0.321809       0.171837  \n",
       "1          0.003400       0.442318  \n",
       "2         -0.159204       0.036596  \n",
       "3          1.629444       1.524243  \n",
       "4         -1.460039      -1.721531  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_df_1_z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vaibhavverdhan/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/Users/vaibhavverdhan/anaconda3/lib/python3.6/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "sc = StandardScaler()\n",
    "X_standard = sc.fit_transform(vehicle_df_1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_standard[:,0], X_standard[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Selecting the values of k with the Elbow Method for k-means clustering')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#Finding optimal no. of clusters\n",
    "from scipy.spatial.distance import cdist\n",
    "clusters=range(1,10)\n",
    "meanDistortions=[]\n",
    "\n",
    "for k in clusters:\n",
    "    model=KMeans(n_clusters=k)\n",
    "    model.fit(X_standard)\n",
    "    prediction=model.predict(X_standard)\n",
    "    meanDistortions.append(sum(np.min(cdist(X_standard, model.cluster_centers_, 'euclidean'), axis=1)) / X_standard\n",
    "                           .shape[0])\n",
    "\n",
    "\n",
    "plt.plot(clusters, meanDistortions, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Average distortion')\n",
    "plt.title('Selecting the values of k with the Elbow Method for k-means clustering')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3, n_init = 15, random_state=2345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=15, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=2345, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(X_standard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.2339617 , -0.57387481, -0.30776905, -0.03041322,  0.2050726 ,\n",
       "        -0.11653151, -0.45904181,  0.32506329, -0.48798836, -0.53604446,\n",
       "        -0.41380935, -0.46366305, -0.60155031, -0.61924057, -0.06177969,\n",
       "         0.01066762,  0.81278556,  0.69897299],\n",
       "       [ 1.13076532,  1.17094237,  1.19500584,  1.01909505,  0.21505188,\n",
       "         0.34250798,  1.27180461, -1.19061224,  1.27548416,  1.09266768,\n",
       "         1.22038121,  1.28132727,  1.07954232, -0.02879497,  0.16229031,\n",
       "         0.26566456, -0.00535553,  0.18380067],\n",
       "       [-0.91987072, -0.52009251, -0.89320752, -1.06435572, -0.50042171,\n",
       "        -0.2190738 , -0.7791213 ,  0.86589322, -0.74535635, -0.484632  ,\n",
       "        -0.78240328, -0.78341064, -0.38498232,  0.83878747, -0.09524349,\n",
       "        -0.30171562, -1.05420601, -1.11069466]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate the centroids for the columns to profile\n",
    "centroid_df = pd.DataFrame(centroids, columns = list(X_standard) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## creating a new dataframe only for labels and converting it into categorical variable\n",
    "dataframe_labels = pd.DataFrame(kmeans.labels_ , columns = list(['labels']))\n",
    "\n",
    "dataframe_labels['labels'] = dataframe_labels['labels'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining the label dataframe with the data frame.\n",
    "dataframe_labeled = vehicle_df_1.join(dataframe_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10</td>\n",
       "      <td>162.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>159</td>\n",
       "      <td>176.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>170.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>50.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10</td>\n",
       "      <td>207.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>158</td>\n",
       "      <td>223.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>160.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>44.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>52</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144</td>\n",
       "      <td>241.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>183</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>93</td>\n",
       "      <td>39.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>8</td>\n",
       "      <td>169.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>134</td>\n",
       "      <td>200.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>195</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>89</td>\n",
       "      <td>46.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>11</td>\n",
       "      <td>159.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>159</td>\n",
       "      <td>173.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>197</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>106</td>\n",
       "      <td>54.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>12</td>\n",
       "      <td>222.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>173</td>\n",
       "      <td>228.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>201</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>86</td>\n",
       "      <td>36.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>7</td>\n",
       "      <td>135.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>124</td>\n",
       "      <td>155.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>195</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>85</td>\n",
       "      <td>36.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5</td>\n",
       "      <td>120.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>128</td>\n",
       "      <td>140.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>813 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0             95         48.0                  83.0         178.0   \n",
       "1             91         41.0                  84.0         141.0   \n",
       "2            104         50.0                 106.0         209.0   \n",
       "3             93         41.0                  82.0         159.0   \n",
       "4             85         44.0                  70.0         205.0   \n",
       "..           ...          ...                   ...           ...   \n",
       "841           93         39.0                  87.0         183.0   \n",
       "842           89         46.0                  84.0         163.0   \n",
       "843          106         54.0                 101.0         222.0   \n",
       "844           86         36.0                  78.0         146.0   \n",
       "845           85         36.0                  66.0         123.0   \n",
       "\n",
       "     pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0                    72.0                       10          162.0   \n",
       "1                    57.0                        9          149.0   \n",
       "2                    66.0                       10          207.0   \n",
       "3                    63.0                        9          144.0   \n",
       "4                   103.0                       52          149.0   \n",
       "..                    ...                      ...            ...   \n",
       "841                  64.0                        8          169.0   \n",
       "842                  66.0                       11          159.0   \n",
       "843                  67.0                       12          222.0   \n",
       "844                  58.0                        7          135.0   \n",
       "845                  55.0                        5          120.0   \n",
       "\n",
       "     elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0             42.0                    20.0                        159   \n",
       "1             45.0                    19.0                        143   \n",
       "2             32.0                    23.0                        158   \n",
       "3             46.0                    19.0                        143   \n",
       "4             45.0                    19.0                        144   \n",
       "..             ...                     ...                        ...   \n",
       "841           40.0                    20.0                        134   \n",
       "842           43.0                    20.0                        159   \n",
       "843           30.0                    25.0                        173   \n",
       "844           50.0                    18.0                        124   \n",
       "845           56.0                    17.0                        128   \n",
       "\n",
       "     scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0              176.0              379.0                      184.0   \n",
       "1              170.0              330.0                      158.0   \n",
       "2              223.0              635.0                      220.0   \n",
       "3              160.0              309.0                      127.0   \n",
       "4              241.0              325.0                      188.0   \n",
       "..               ...                ...                        ...   \n",
       "841            200.0              422.0                      149.0   \n",
       "842            173.0              368.0                      176.0   \n",
       "843            228.0              721.0                      200.0   \n",
       "844            155.0              270.0                      148.0   \n",
       "845            140.0              212.0                      131.0   \n",
       "\n",
       "     scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                           70.0             6.0              16.0   \n",
       "1                           72.0             9.0              14.0   \n",
       "2                           73.0            14.0               9.0   \n",
       "3                           63.0             6.0              10.0   \n",
       "4                          127.0             9.0              11.0   \n",
       "..                           ...             ...               ...   \n",
       "841                         72.0             7.0              25.0   \n",
       "842                         72.0             1.0              20.0   \n",
       "843                         70.0             3.0               4.0   \n",
       "844                         66.0             0.0              25.0   \n",
       "845                         73.0             1.0              18.0   \n",
       "\n",
       "     skewness_about.2  hollows_ratio labels  \n",
       "0               187.0            197      0  \n",
       "1               189.0            199      0  \n",
       "2               188.0            196      1  \n",
       "3               199.0            207      0  \n",
       "4               180.0            183      2  \n",
       "..                ...            ...    ...  \n",
       "841             188.0            195    NaN  \n",
       "842             186.0            197    NaN  \n",
       "843             187.0            201    NaN  \n",
       "844             190.0            195    NaN  \n",
       "845             186.0            190    NaN  \n",
       "\n",
       "[813 rows x 19 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_analysis = (dataframe_labeled.groupby(['labels'] , axis=0)).head(1234)  # the groupby creates a groupeddataframe that needs \n",
    "dataframe_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    297\n",
       "1    251\n",
       "2    232\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_labeled['labels'].value_counts()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, '3D plot of KMeans Clustering on vehicles dataset')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 3D plots of clusters\n",
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=20, azim=60)\n",
    "kmeans.fit(vehicle_df_1_z)\n",
    "labels = kmeans.labels_\n",
    "ax.scatter(vehicle_df_1_z.iloc[:, 0], vehicle_df_1_z.iloc[:, 1], vehicle_df_1_z.iloc[:, 3],c=labels.astype(np.float), edgecolor='k')\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "ax.set_xlabel('Length')\n",
    "ax.set_ylabel('Height')\n",
    "ax.set_zlabel('Weight')\n",
    "ax.set_title('3D plot of KMeans Clustering on vehicles dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us try with K = 2\n",
    "final_model=KMeans(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.fit(X_standard)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=final_model.predict(X_standard)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Groups Assigned : \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Append the prediction \n",
    "vehicle_df[\"GROUP\"] = prediction\n",
    "print(\"Groups Assigned : \\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compactness</th>\n",
       "      <th>circularity</th>\n",
       "      <th>distance_circularity</th>\n",
       "      <th>radius_ratio</th>\n",
       "      <th>pr.axis_aspect_ratio</th>\n",
       "      <th>max.length_aspect_ratio</th>\n",
       "      <th>scatter_ratio</th>\n",
       "      <th>elongatedness</th>\n",
       "      <th>pr.axis_rectangularity</th>\n",
       "      <th>max.length_rectangularity</th>\n",
       "      <th>scaled_variance</th>\n",
       "      <th>scaled_variance.1</th>\n",
       "      <th>scaled_radius_of_gyration</th>\n",
       "      <th>scaled_radius_of_gyration.1</th>\n",
       "      <th>skewness_about</th>\n",
       "      <th>skewness_about.1</th>\n",
       "      <th>skewness_about.2</th>\n",
       "      <th>hollows_ratio</th>\n",
       "      <th>class</th>\n",
       "      <th>GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>48.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10</td>\n",
       "      <td>162.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>159</td>\n",
       "      <td>176.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>197</td>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>9</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>170.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>199</td>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>50.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10</td>\n",
       "      <td>207.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>158</td>\n",
       "      <td>223.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>196</td>\n",
       "      <td>car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>9</td>\n",
       "      <td>144.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143</td>\n",
       "      <td>160.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>207</td>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>44.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>52</td>\n",
       "      <td>149.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>144</td>\n",
       "      <td>241.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>183</td>\n",
       "      <td>bus</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   compactness  circularity  distance_circularity  radius_ratio  \\\n",
       "0           95         48.0                  83.0         178.0   \n",
       "1           91         41.0                  84.0         141.0   \n",
       "2          104         50.0                 106.0         209.0   \n",
       "3           93         41.0                  82.0         159.0   \n",
       "4           85         44.0                  70.0         205.0   \n",
       "\n",
       "   pr.axis_aspect_ratio  max.length_aspect_ratio  scatter_ratio  \\\n",
       "0                  72.0                       10          162.0   \n",
       "1                  57.0                        9          149.0   \n",
       "2                  66.0                       10          207.0   \n",
       "3                  63.0                        9          144.0   \n",
       "4                 103.0                       52          149.0   \n",
       "\n",
       "   elongatedness  pr.axis_rectangularity  max.length_rectangularity  \\\n",
       "0           42.0                    20.0                        159   \n",
       "1           45.0                    19.0                        143   \n",
       "2           32.0                    23.0                        158   \n",
       "3           46.0                    19.0                        143   \n",
       "4           45.0                    19.0                        144   \n",
       "\n",
       "   scaled_variance  scaled_variance.1  scaled_radius_of_gyration  \\\n",
       "0            176.0              379.0                      184.0   \n",
       "1            170.0              330.0                      158.0   \n",
       "2            223.0              635.0                      220.0   \n",
       "3            160.0              309.0                      127.0   \n",
       "4            241.0              325.0                      188.0   \n",
       "\n",
       "   scaled_radius_of_gyration.1  skewness_about  skewness_about.1  \\\n",
       "0                         70.0             6.0              16.0   \n",
       "1                         72.0             9.0              14.0   \n",
       "2                         73.0            14.0               9.0   \n",
       "3                         63.0             6.0              10.0   \n",
       "4                        127.0             9.0              11.0   \n",
       "\n",
       "   skewness_about.2  hollows_ratio class  GROUP  \n",
       "0             187.0            197   van      0  \n",
       "1             189.0            199   van      0  \n",
       "2             188.0            196   car      1  \n",
       "3             199.0            207   van      0  \n",
       "4             180.0            183   bus      0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bus</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>car</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>car</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>van</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>813 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    class  GROUP\n",
       "0     van      0\n",
       "1     van      0\n",
       "2     car      1\n",
       "3     van      0\n",
       "4     bus      0\n",
       "..    ...    ...\n",
       "841   car      0\n",
       "842   van      0\n",
       "843   car      1\n",
       "844   car      0\n",
       "845   van      0\n",
       "\n",
       "[813 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicle_df[[\"class\", \"GROUP\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use ward as linkage metric and distance as Eucledian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(812, 4)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### generate the linkage matrix\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "Z_df_average = linkage(X_standard, 'average', metric='euclidean')\n",
    "Z_df_average.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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6ddCqbdtbktxS/3170zQfSPLwJIJWm8AWjsxT39svzWNru1XvTPp8OPCmcDfqfC1iyzTbsLEM677aht2te3+7375uXY53N9tNcizTJM/vWvVx7G6WcbOOLdK2t1swzypMIFneSr1xmzAGYX4mnf+eZ5leVFld0PXoZddflmtvu/a+f89qLs+0aprmgiRPS/KOeXweK8IWjszLELZfmrVjUQ9guRaxZdqytmETHNtsq7LaZtJVGolyPASeXTgc201yLJsg4vLYIu3+dgvmWYUJLMqytuZnufZ6fvcirdv894KuR6+48Yo0dSv+K268YtZUzh60aprmtCS/neQftm37hW1+/6okr0qSi889Zdavg/lzB9gwDGH7JWC1LGI7r4OHSvt+5PIkbekH5j0o9owiVmG1zSSrNBLleCg8u3BY1mGSw40WzGLSYN4y6oqbMobL3AzzsFvA/PZbRv/uytRWd36h/G63wJcyt3x7Pb+bySzoevSisy6a6f3jZgpaNU1zQkrA6jfatv0f272mbdtXJ3l1klxywRntLN+3EAbhuAMMgHHLCCp5RhGrYJLJRuV4ODy7cDk25TpyqDdaWFXItBZxU0afKwDWibkZ5mW3Cfk7Pj3dc7Q6ylx/1uFmIPZt6qBV0zRNkl9O8oG2bX96fklasmUMwid5aBv92K5D23VJ8djd99sxSAVYbYJKAJttr6DUUIM5izDEPtGqQmYx75syrACYnyGuzmO9eDREP3YbV23KjUBMZJaVVs9L8jeTHGma5ur6s59o23b2TQvnaT8Bo0UPwid9aBvDMM1dPslmDlK3C/Btt3x/HTsdnSubYtKtVNSD+Zt2y5TE+WD+FvQAXwZk08c4+wlKDTGYs0nWeVWhLexWjxUAQB+m6S+S5fcZu42rNulGIPZt6qBV27ZvSerTtYZsKAEjA5jVNM0dGJt4jrcL8G0N7K1rp7OsztUWKMsx70nYaSf89kpHZ5nnf5KtVNa1vvfNzRT920/d3JR2eUEP8GVATCAIStEfzxVkiNywAsMzaX+R9Ndn7Dau2vQx1yYvBtjBTM+0WhkCRsNgn+f1tleAbyj1b78dQbL/MrqMztUWKJObJtA370nYaSf8dkvHLOmZ1X4D+UOp7+vIzRT92qtublq7vKAH+K6sdVyZtOkTCNCndX+u4CTXZcnqt6frwA0rMExD2FZzHcfBu5l3H7bJiwF2sD5Bq0UHRObxXKpND9pMu8/zeEPgTh5mtZ+OIBlmZ7DOW6AswrSBvnlPwk474bcqgeB5sJKQ3QxtddNudXOd6iWTszKJVTG0dnUv49fRng+9PvZ7XZZoT3ez67O4s/cWYclk9d0NK7BZ9jsnu2nj4EX0YZs0B7QP6xO0WvSDL+exzaCHc0636m28Idhr8nmnxnRIF13zMuvgdB3zZL/2cxfKhnUGa0ugbzWs4krC3QbvyWa3sfNmdROrxMokVsGqtavddXT372nStWl3gK8Kq/hnt9v20cnuW4Qlw6vvwLBMMic76zh41fpqfdhCrU/QKtk9IDKPVU7z2GbQVoXT2a4h2C4Pt2tMFzEI2y1gNO87mXYyy+B0mQPTVet0gH6sWoBxp8F74uJ/ETZhddOmr8gHlmvV2tWdrnn2a553gHuuD0MzzfbRnSHW93Ww103G87CfuadZac9I9j8nO6tNW601FAO9IXe9gla7scppcyxj4nO3gNEy72SadnC6zIGpTodxy96+guXZpJWunaFuj7KMbZ8W8XzATbfpY9V5bQc9y3aj89gOHBiuea2E3G6l2u23lLbjzi+Uv49crv+DTbbXTcbzsNfc06w2cTxK/+xasHwDvSF3c4JWiVVOzNcqBIyGQqdDx/YV62tZK13Z2zK2fVrl5wMO2aLHqkMOykyy9ci+PmeKz9hpO/BVXTW+quney7oeF6tluxsl7/h08sjnl//r/4BZVsANwarN3Qx5nAvLMm09GOANuZsVtIJNNq87mJnc1odHb7pV277C5Nj+rdoWf+tsGds+eT5g/ybdUnAez2hdpEm3Htnav3bHMktbtF3gcFVXja9quvey7OMyCTadTVyR60Y9gP7MOs7d75aOk2zLuC79G6tj6Nd7ExhG0GrVJ3RnfQbBoi6ETLT2a6cOb7cObpHnZ153MDO5rQ+P7uxU97f7OYu1Wzu8rpN+wOqbZkvBddp5YGv/ush2edmT0fO62WhdJ9GXeVxDuvhfpes7K3JXh5sb72+Rz/eQ39OZZn4lkaebZpZx7n63dNzvtoxD6982cTv/TbUm13vDCFrtNKG7KmZ9BsGiLoRMtPZrpw5vpw5uGednWQ9P5P62G/jsVPe3+zmLtVc7vNvk2Kw3LgDMYmgXJctuExf5rIg+udloWIZSz1bt+m67a4/7TTy3pc0wad+fSdqbVQqcbjVJ2hf5fA/t+3QmnV9J5Ok0FhmwXQXz3NKx7zHDVuu+nb95mbUzjKBVsvoXnLNeyEzy/kmi4+t6d+WqmKTDc35W3zSd5E51f7c2YZUvFqexrLsRp23HZ71xAWCdaBPnZyg3G81rV4g+VhdsDZCswwTcql/f7fbcxWW1GduN2TfdftubVQucjps07Yt8vsdQ2vdVM2lAQZ5ObpEBW+Zrmnmhdd7O3zXI/AxkVd5wglbs37pGx3d6LgHsZGhbKyyrk1zli8VprMLdiEO5A5v1sp/J1mT1JlzX0dBuJuj7GTzaxPUyr10h+ujPdwuQLPq72VnfD/vebszO/q1y4HSV0z6rSbfXM77cXItoo9fxOYd7PQNr0VtX9jkvNNT5252uQXa7VhvaddwQDCTuIGjV6fviflLrGB2f93MJ1rHhWVaQZlXqwxCDGcuaqNu0Cy53I7KJ9ppsTebb3tlSYXrLumjc73Ngh/QMnk011Iv5ac1rfNNHf77bnfnGEptLcH34BnKn99qYZHs9AX3mbR2fc7jXM7CWsXVlX/NCy3yu7Dzsdq22aTeF79cA4g6CVp1lXtzvd8JhE81zm8hFNDx9B8KWFaRZpckuwYzF6LusD9W6TUKukk3tO/faBmWe7d28VosObRXssizjonGS58CakO3Xql3MbxrjHNbBKj57ZpIxwkDu9F6oZQfm9ru9nrEDi7Cf8rdqZW/aZ2Dt9ziHPF5Ztcf87Hattmk3ha8IQatxy7q4n2TCYdFmvat6mknEZd7JPe+GZwgR+GUFaTZ5sstqg2GU9SGaZRJSuZrNkPrOdTaPtn/RN1jstW3iEOvWPCel+rxA3NSA5LRW7WJ+mfpeQWGcs15WZZeIeVvFZ89MOkbYz53eqzzGniYwt4rjoFW019Zve9lra7j92oTzaXy5s/Hxyu23JDe9ZZRHy7DK7es6WMdns05g2EGrdb6jfSgXsXvdVb3XBcA0k4ir8HC83RpmEfhi1QYWk9yhsgpldBmU9e1N234PrVxtVyeSYdbfzrL7zk2dBJuHRd5gsdu2iUOoW9uZ9m7xoa0wHOK2vKtuU9uZIaygMM5ZH6u0S8ReJr2rvu/ng01j3mOEoY2xx+1nTmvSLZiGMg5a9+DZXlu/7WW3reH2a2jleVGML3c3flPhHZ8uP1vWTZxDbl83wYY/m3XYQatV21ZjVSPQu91VvZ8LgGk68aGv4tEw723VBhaT3lE79DI6D0Near6uhlSuttaJZLj1ty/rNAm2btZhkm4/aR3iCsNlrfjelD5qk9uZAeyVzwSGHmDdbYw1tBsAdmMV4HSGNMYet6g5rSGMg4YSPFukabd+m5ehledF6vuxD6syn7vd/Ouix8xDa19XoU+f5wqpndqhLh+OXjPsMjuDYQetkuGsSNqPdQ10DLGBWsYF07KOexVXO3QmHVj0vXrSHbXHWsZF8YYvZx48E4Z7229f0PdWV5tunQMcixgL990f78cmTdwObazNZttpMmiVA6xDvAFgN65ZhmeWcd4qzWlNagjBM5iHVZ7P3aQxc7IaffoyVkitcpndp+EHrVaNi87FW+ULpu1s0mqHVVs9uQkWfVG8XWd9+y2ji707v1D+feTy9ZlgphjSpPhOE3DzvEtrXtu/rWMdWEZAadMu1ma1Kv2xidv52OuGr1luCNukgP2Q7uxdZLu622TQXquZhrxV2DoHDlbZ0FfwdYawpSmwWKs8nzvNmHmVr0NXoU/fbaXmvMrYKpfZfRC0YjWtW8XcpNUOq9C5rLqhrd7brnzf8eljy4KLvv1bpYv7oUyK7zQBN++7tCZ9WPh9E3xT5NOk5aDP5xAuK6AkwDGZvvrjVb5A3s0QAhrTrpCZ5YawTZrIHdKdvYtuV6dpHzZhq7D9GNo4fOgmaX/mtX3XtEHfTbpmn5TdNWD19Hm9PoRxM4O3WkGrdd76BdjeJBcn2ohiFVbvueib3iqtNh1SkHqntCw7jdtNes40ObjPctD3cwgPHirt8ZHLk7Rrvfc2exhSQHuehhDQmHaFTPf7Q68Y7Y0/yQTCJvXpi+ozphnDTntX9TKee7HVOpeJrVZhHL4fy3y+y35vSJ3XVkiLDPruNRE79InaaduIZWyFtV993qgFq6av6/U+x81Db4e5z2oFrWz9wrytysMW+zKE/Jnk4mRV24hFdJrLnEAaQjnZNMtcbTqvO4YXVU76WHk260qReV0cTFoO+n7A8aq10eu6ImgIxuvAqqwe3Y8hBOpnScMQAm+raB5leFnt46q1w6uqj0DuvLdnHOqzMvbaonLrNdVO11iLWp29Vzs69HZ2ljZiGVth7UffN2qtEyvo5s+N1iObGDBbpp3mX1boGrefoNUsE7R7DS5ETNnOTpV1aIPxoXVgk+bPohq/SSZm99NG7DePlxWMmaXTHEKHM7R6tKqGelfgvO4YXlQ56WPl2bquFFmGVdrCz3lejlVaPboJhhB4W7ZZx3vzKsPLah9n/Z4hjD37uuYf2rXauEVsz7hqW/Jve03Vw8TkXu3o0NvZVRqr7aTvG7XWxZBW0M1qHjdi7ieIt9dnunlkf7a7IWieht4Oz8NO8y8rdI3bT9BqkVHNTYmYMpndJkvnORifddJ50g5sGXumT5I/q9D4TZLHywzGTNtpDiXPl31Ru46ru/q8K3Cvu8TndcfwospJH5MqmzDQndQqrpjZawJy64qgdWt3On1PQi+qDq9imdxEfT8DaB7jvVWb3J/FEMaefV3zD32ycZnbMw61fd06PjNeY6i2BkC2s1NQZKtlbN+5naH2eduNa+dxI+ZeQbz9fuY6BIYXbbsbgpjcTuPTFekb+9secJEZtCKZzx7mPRBeRHBqx45whknnSTqwvvZM323ibhXq3yR5vAqTEKuQ5/M2zSrAZd8ZO81dwH3dFWilA/u1W11aRDladDCljxsZhjjRN4RJ6EXQtq2GWcaz81pxswrjvSEZwtizrzSYbCy0r6ybZW+FtzUAsp3tgiJbDS14PgQ7jWvncSPmbkG8aT9zKBa5ummWuZFVzlNmslrPtBpnG8D5WsQDQ2c9R0MeCO+WtmVPOs/S8U5717jt4BiCSVcBLvvO2FVb+bvqg8K+V4r0ZZbjnqaf3qsuzbscLSOYsuwbGYY6vhnCJPQirHrbtimmHc+uWl8Lkxj6Ct9Jni81z3SbC5pM36tZV0UfW+HtFQDZD+Ob7a3ruHaRFrm6qc/x2pD6jCHevDhgqxu0mrbAD6mwDskiHhg6j0ZpyBMNQ07bfs0SfFqH49+voV8wsj993Bm73WB5GcGVZfd129WRZVvXlSJ72eu4dxsYT9tPL7sureMWfZvUh66iRbSh61J2h8jE1Gob6jM9h2DWGwX7DFYsclw2hGD1Ks0r9bU7yyoa+lZ4220pOM1zlZifZa/QW7R5XaNs10b2NV7bb5+xjIDSUG9eHKjVDVol0xX4IQxwhmoRDwx1ETl8fU6cDflhxuOsLJvOrBN0Q7gLZREXpMsIriy7r9uujkxrlvO+qX3Obse918C4jzybpe3XHrMMO7WhfZXdnSb1hzhmWrRJ+mWBwtXQ5zM9F2lewbhZrtX6DlYM/ZEQs4w5V21eaV7Pp6Vf220pOO1zlZiPPlborYKhtZH76TOWFVBy8+K+rXbQalqLnqAZwh3uq9Ag7jef5v06hmPoDzMet13Hsk5lbh7HsvUzZp1cHsJdKIsabC0jULDsYMS8Bl9DOO/rZmgD41nb/qEdz6abV184tD51uza0r7K73aT+tKsuFn3NsOjvmKRfFuQehv3U7b6e6blIQwnG7RasGMINYn2adcy5qTdL0a/9bCm46u3nqhn6Cr2+rGIbud+x+qrOw6+YzQxa7WYeF8xDuMN9FSrLfvNp3q/bNEO/GFnUNlPLmPxa5TK3U4Apmf6u8u0+Y9bJ5SFMTq/iYGvVDeG8r6qhTfzvxIPsl2OV+sJV6VP7KrvzuEt+GdcMy/iOSfpl/Un/VqVuL0JX/o7ZPqot7fIQJrnmcaPQoibulrX1tDZicXZbbZjsr6zYPnTzbD3n93w1+XePGP1+/CYAZeBYQ5/7W5ZlXv+s2jz8ihG02mqvQfV+n0Wyqne4L9t+82ler1vmZN5QtnLZ1FULy7pA3s8zi4ZopwDTdq+b5K7ySdu+WbeIXOc7XDZ90LkK9WiINnlycFbrWOb67Avn9TnbtYVMbxnXDEO/LlnHuj50m34D0LxWSy7CrPV1URN389x6elUMfev8SdvOnVYbJvsvK0NZsThkkzznKll+mZq0XG8950evSb56R3Liqce+Thm4v02d+9tqmdc/QxjvrvG4VtBqO3s9G2JZD3rfb4CM/VvmZN6QLk6G0pgu29Af9Ni3SYLBi7qrfNatluZ9oTykDn/TB52rUo+GaNMnB6e1rmVu1cvDdm3hJhv6pOYqWNe6Pql1vvFniBb5TKG+5w32c625181Y243BN+0aduhb50/Tdu60bdok53SW7UM3YaXWfp9zlfRTpqYp19vNP6zbFrL7Mc3cxKa1mzuZ5fpnCHNCs+54tCYEraaxrIv/7QreJt91Py/LnLzxwNPhWvQqllWfJFymWYNi8xyYzdLhL2Jws6hB5xAGYvuhHg3bEMvRrBP6ytwwuQAfGfqk5k4meUbtMq531PVhbG2zU+BsiP3LVkNK4zJvrJ3WXjdjrfGk20SGvn3yqrWdQ1mpNclqqGmCaft5ztV237UsQy/XQzWkdnGvPm9IfeKslpHve91sMs8djyY9d1u/o8cdgASthm684G36XfdbzbNRXKcGdhqbePzqEzuZ9mJsSIPKvaxSWodgE9vI/RhiORrihP66lB+rMoZjFSd/JnlGrfHZ/s06kdB3QHinwNm0/csy29uh9YH7Gb/23R/tVd5WLSAyNPvppzdxte4sK7XmZb+roYYwbt3LJqxem4f9rC7dT10cSru4V583tD5xVovO9/3cbDKv8f6k526nANqp5yS3fSh5/b8sP19CXRe0GpL9DCL7vrAYknk2iuvWwE5qFY5/EdteqE/L0fcF8jINZVC5H6uU1r5N2kZu0vPIhliODh4qF31HLs99D7zv8xysQh+7H0NYlcGwTDo2m3RbYuOzva1DkG+n8z1N/7Ls9naIfeBu1qU/WrS+t1uc1n766b5v7lmlMfLW1VHbrYyadDX/XquhVqHfG8rqtaHbz+rSod1ot5e9+rxV6xP7tuxdwObx+zs+Xf5e0njzuIV/A/t33yCyMYjcr4OH5vvw762f1Q1YuwmvdTZNXu6VP5Pk3/hrD7/s/q/fq35s0rmaxFWHS372mTf7bducQ4ZskjbymIuQFezT92qPV8HQzsE8xyt96iZc1uFYmJ1rl2FQL4+1Lu3tLHYb/0+aP5s4Pl/ltm0/7UH3mr1eN+2531r+xseSQxuf7aZLa+fgxfcP0gw5/Ys0Xob2U5aGZrc2cr/XQbuV885e9XG/dRGGYsljLCuthmYVlvVvkr3uRlvmuRjiPqPzXCK8nzvDdqsf7hzc3lDuwN1P27asvYNX5e4+VtssKwX63rplXVbULOuZcNPmjfHc8tjacD62K7MmWei7z+L+5jn+39RrrCG2bdvVtWR+9W1rGz/tud9rBckqjZF3Wx21CquilmEV+4Dt2sjx3+3nOmgVV0rBirHSahVNc+fPJt4hNS+7RZInWUGy110Y271n/Jzt9V193bW0V6R9kkj8rHeKurNye6t0B+6iz+Eq3d23KfRP9zdeTre7mFqGbou98e31KOZ1B/aQ7+Reh9V247q8PvWc5Ka3lL3YV/2Y+jDkMrsuhrBCflJD6LO4v3mO/11jDcPWupbMt75t18ZPe+4XtYJkU9ubIfcNq3pOdmsj99t+rspKqSGXH9iFlVaratIGcVPvkFqG7c7Fdncp7XUXxn7ubNpvp+mun/XlzvzZDa2eTLv6q4/99hexUk3/tL2unG4NHvSx4qr79yreObioNnOeWxMP0XZ3ma667fZiH5JV6d/nXWaHdNyzpmWS9+/02nmtkFn2GGFeDwvfBFb9M4utK3/mXd+GOi4Zt4ntTR+7p0zSj2ziOVklQ9l9ZxPo4+fKSqtN4g6p5dnpLqXd7sKY551NrK9p73Je5dUsq5z2/Zh29Vcfd7wvaqWatm5n9wUPZrxzcdqVM6t+bqwMmd5Od5mueps81DK9qWV1Gce93zI7a1omef9ur53HCplNLU+rwKp/mH1F9ywrR2b57u57D78s+fhVyc1XJv/uEaOfLXJctOzdU/Qj+7Mqq5hWafedVTZtH7/q11cLYqUVLMq0y+hhL9OUk1VezbIOqz32stvqr93u3O6jzRjaSrVNMI88X8eVM/u1CX3rMlerrHJ/MgRDa9MXZZIyuejjnqTMzpqWSd6/6ONep/K0btZxLDWkVZMM36zPT51l5cgs333f9x5Kjj8xueeu0e/W8ZlGQ+xHhvYMLauYNttOz3s99IrJdktxfbUtQStYVS4Mlm/V83yIg879WuW0z2qIA5hVrwubah0nySiW3U5scpvcmbYdHGKbvghDO851L7P65fU2fn77nqDdydDq/CQ2tf70fdyzjktnef883zuPbfEWsaXYfY+cGDPejnSG2J7sZj+P3li2VbnG6rvOr6Od+r5pguPrPladgqAVrKplXhisc+c2yV7Nq3wxNkTrXK7mbWgDGHUBhmdo7cS6m6Ud3JRztSnHuQiTBilWvV82JtzdrKtSlmU/dX6I53pV68+seTnk416l58LMI62LWK0zviqs0wV6OstsT+a5QmoZz9Darn4Nre2a1JDr/Crbqe9blWDmgAlawSpb+n7GGU7nNq8Lnkm3njMBMz9DLFfs37R1YYiTFeyPc8ckNqG8GBMsziaUn93M+w7d7SZVh2QVxoTLKJN7bR+6DpNfQz3Xe9WfrRPtQzCPvBxqP7ZKW67tltbtAjXJ9sGaRdTx8eDOdpbZngxxhdRutq1fA2y7JjXUOg/bELQC9mdonds8L3iGdmybRN4PyyQrD6c11MkK9raIc9eVuXu+Wh5kffDi/U0GDXlCe8hpW6Y+6rq8Xx/6ivlOYG43qTo0Qx8TLqNMbkq5H/q53mrIzwUdel7O0i+vUqB2p7RuDdQkuwdrVmEr0FksY4XUdqYth1vr1zT1rY+xqfHw+lilVacLIGgFrK6hD9Jh1Uy68nBa61Z3N+nCYN7nritzx5+YfPWO/U8GDXlib8hpW7Zl1/V1zPtNal+2Wre+om+rNAE8VMsok8r9MKk/0+mzX572xqh527raabcytMytQNc9QDauz3LYx3ev43h4U/Wx6nRA1x6CVrBuBtTAsGaUrc1gsmRyLgxmM22ZG3JZHXLa1t265f0iVzh2/waAediuf+mrX572xqi+LStAOsRn5U2668ck45k+x4d9fPe6jYc32bJvmhjQ3IagFaybATUwrJkhl61lbGsHu3FhMGJCHOZrUSsc7/s3AGtvGeOzefcvs6bZ+Hx3Q1tBOOmuH8YzsBgDaTsFrWAdbdfAmNRnHgbSed3Psra1A/bmAhIWb7s97icx1P4cgMVcuy9rfDbP/sWYcvNMWn6MZ2BtCVrBpjCpz7ozYGWdrdrqJfURFmu7Pe5ZP8to++f1HRv+sPBB2u+5dXPj8GwXrJnHOVrF8dkqprkvXTucHPu8qmT2Z1Zp43c3bV86601IsMaO6zsBwBIdPGTQx2boBo1HrzHwm4V8HI77Ji8ak9NA0W3rY2y3vpbR9s/rO8YDqfqqYdjvuTXGGKbxa/dJztEqjd/7TOtO333V4RLomXe6tn7u4ZfN/tlbj6Frh5PSFh+8uPx7Hje3aON3N207Kl9hR1ZaAcOyaqsJGKa9tpIYYjkbYppsyTEsJqYBNs8y2v55fcfQno8yZMta3bTfc2uMMXz7PUerNH7vM607ffd2K5nntT3jfcGJjIJLs3z2dsfQtcPj5tUmz6uNX9dVoNO2o/pO2Jag1boa4uQn7McqDbIZtt0GjUMsZ0NMU2ISY9n036vHOQNgJzv1EbZuZ5FWafzeZ1p3+u7tggjzGO+NB5TmGUgagkm2D9zvda92Eorx9ufwy8p2nxtA0GpdDXXyE5Nb+zGUgdcqUr72b4jlbIhpYrn036vHOQNgJ7v1EZOO+4zzoT/Ge7ubdIWaVaCwf137c/DQaJXmBhC0Wmca92Ey2GGRlC9Yffrv1eOcAbCTefURxvnQr93q8qptZTet3YLntrmDY83zZpMNrF+CVtAHk1sskvIFAADrxzh/PVlFN50hBYo2ZSs7wXPYP/VlJoJWAAAAANAHE5vTGVqgaEhB5e4ZU8n9n4Uznk/TBEyHdJwwdOrL1AStAAAAAKAvJjans4r5tlegaB4r7+57xtSh8pypZPQsnPGg1SoFTK1IhI0iaAUAAAAAkCw2QLJXoGin30+apu4ZOOPvPXrN9s+iWgWrFGADZiZoBQCwCOMXlt12HAAAwLAtOkCyV6Bou9/PkqZlBXwWvRpqVQJswMwErQAAFqG7ODx4aLQdBwAAMHxDDJDMkqZlHI/VUMCcCFoBACxKty3H4Zf1nRIAAGDZNu1ZTEMM9gErR9AKAAAAAGDerD4CmJigFQAAAADAIlh9BDCR4/pOAAAAAAAAAAhaAQAAAAAA0DtBKwAAAAAAAHonaAUAAAAAsOquOpwcvab8uepw36kBmIqgFQAAAADAqjtyeZKm/Dlyed+pAZjKgb4TAAAAAADAHBw81HcKAGZipRUAAAAAAAC9E7QCAAAAAACgd4JWAAAAAAAA9E7QCgAAAAAAgN4JWgEAAAAAANA7QSsAAAAAAAB6J2gFAAAAAABA7wStAAAAAAAA6J2gFQAAAAAAAL070HcCAAAAgDVw1eHk6DWjfwMAwIQErQAAAIDZHbk8STP2bwAAmIygFQAAADAfBw/1nQIAAFaYZ1oBAAAAAADQu5mCVk3TvKRpmuuaprmhaZofn1eiAAAAAAAA2CxTB62apjk+yc8n+ZYkT0zy3U3TPHFeCQMAAAAAAGBzzLLS6llJbmjb9sa2bb+a5L8n+fb5JAsAAAAAAIBN0rRtO90bm+YVSV7Stu3frv//m0m+pm3bH97yulcleVX970VJrps+uQAAAAAAAKyoR7Zte85Ovzyw6G9v2/bVSV696O8BAAAAAABgdc2yPeDHkzxi7P/n1Z8BAAAAAADARGYJWv1lksc2TXNh0zQnJvkbSX5/PskCAAAAAABgk0y9PWDbtnc3TfPDSf44yfFJfqVt27+aW8oAAAAAAADYGE3btn2nAQAAAAAAgA03y/aAAAAAAAAAMBeCVgAAAAAAAPRO0AoAAAAAAID+tW27tD9Jzq1/n7bl56eN/fvMJI9I0iR5ZpIHbvM5D6q/P5Tk6fU935rk4iSnd59ZX/OMJM9N8tT6/wNJTq2vOT3JCfV33e+b7d635fvPHPueB9bXnzv+u7HPOH2HvOieJ9Zsc/xnbs2fmo5nJ/mOJH89ydlJzt3y3hPG/n1Gfe3Lk5xUv+epSZ68wzk5dTzN4581ls6n1jzp8n67c3PuNu8bP1+P2Jo/Y3n4qC3f1Z2TB2z5zAdvPSfblIsHjn1W9/k7ntst+djUvD0zySX177+W5MKtZXY8bdudux3OzY6v2/KeU3coD+fu8V33O9djxzSe76du894H7/Cd5209h1s+e6dz0qX1kePnZus53fKeB46l9b7j2Foutry+2ZrW3dqhJA+o5/Sl2+TTmWO/v2Brntbf/1CSf5pSN//uWBpP2yFN9yube537Pcr34/Zxzrq6/fDtvjv3bwvHj/GY8rNN2XpqRm3dudu87rRs30527dL/VP89XidPT20Dxj7nSUlOGas34+XutK3fO5bG+7W/KX3Fbm35qdt97tg5fVKSU7YpC/dr47eWodT6n+SijPqrC7dLf33d2dulcez4uvy8r43Yes62nKv7+qjtPnO7Mjf2HVv7p237tO3ydSxt3Tl5Yf35MX17cv82a8s5OaaMj+XneJ14XkZ95P+UUr7G379jfuzymQ/cKY+3KQdd2rqy/7Atn7+1H7vvPeP5UP9+VLYZA40dx2nj/9/p88Y/c5s6ckJG9e6Y+rL1PI+dz/H86/KrKyeP3/K6Lp8eMJ6GjJWzHFv/zh/P9y1t2Hhej7cXO/U7j9oh7c/ekl9bj+l+bUeOHWNemG3GMzuUo2PO0dix3K9t3/K6B+50Lrc7n9ud/635ki1lf5vP/NtJXl3/vGzr63Nse/SLKf1ml96unnTH2x3nTyb5B/Xvf7L1uLOlrd+prG45hjOy+7j6hC1/PyCjfqY7njOTnJPkxIyNoXfLv23K6tb+vasDx5y3HNtuPHXr7+truv75mfVztrY/Xfq6vx/f5XO2tDMpZfVZSf5Fyhjl9LG8e2pGdfLxW9JwRpLvTb1e2JLnTUrbevo2aTuz5vHfTPKq1HZ3m/PWHf943/X4bN/m36/f2emcbKk33djivmPY5rt3LF9bym53bF3+vSrJj9U/P5A92p9tPm+nscr4dcEJ2f6a9dwt+XJ2xspu7t9unzn++rF8eGiOvR57Xn3NoST/c5LvT/L/SfIvtzkn257Dsd9/a0qb8K+S/MMt331fmrY5/vtdB6TUh/Gx02lb0nDfuGuPz96a5vvap2zTFo7l8wnbfd42n39f+Rj72TF9WLbUu7HXbb3efML4+7apm+PX8vcrS1uO45wtadjXuC/37zNOyaicPTw7jA3Gz2Puf03R5Xk3z9K1P8eUnx3O3Xg/P56W52Q0DrjfZ2bLuGSX79j2Wm2b8nBMG7jDOdxxzNidky2ffd91zZY2bLzefPNO9SRb5mu2fs4e5bbLn23HIduUi/E5ovFj6s75/drdLZ8zPkbd6dps2zqXbcb/GdXdp+b+4/2uDHR/bztWy/ZleLsx03gZPGEsH7Y7hvvNCW2pd9u1y9vOg2SH8dpOP98tz/eqC9t8/uO3+93Y77fODZ47/p1j+dCVly7PT9jlc8fLyNZx8/0+I8f2lfe1M9vk0/j18Hj5eXlGY/qX7pSfY+8ZT8PWvO3mBU7Yku496+KE5/R+11Cp7W39jNNz7Fig2fq+bT7zIWOfdb92JMe2kVvboK1t3tZxy9YxQnecl+yQlq3n8H5tQnK/8fUZY+fnfuUhO1xLZdQ+bJ1fOnP8+Le2Fdudq2wpo7vk9dZ6su11TI69Pt5aT87Y5fOPiUWM5+PWc7XNe4+ZMx37rK7d3Wus9eAcO9YaH4M8L9uU7ZQ4xl9P8rNJ/vpe9aT702X6QjVN8+okN6RcGH8wZULmk0m+kpKh59b/357krJRB+adSBq53JvlIkltSMvbBKQ1Em+T/mzLAfneSu1Mu2E5Pcn2SxyX5UH3/PUkOJvly/dz3J3lB/Z5PJ7krya8neX6Sz6Zk8N31fZfXnz8gZcLiEzW9FyT5aJKT62s/V9P1riTfVI/30UnuSPI/6vF9XcqJfEeSF9XvfVRKYfhokuOTfCbJX6Y2JvU7H5/kmvqdb0i5+HhWkqtTJkH/qn7Op1OCA7+U5PtSLiB+PsmHk3wsydtTLmRvr8fZ1jR+uH73A5N8of7sz2seHqh/PlRf8/QkX6o/+3LN/79K8v9LaUDaJDcn+cN6rN+eMsB8f8oF1mPqe2+o/39IzZuHpJSF2+vn/1JKh3JRTddnkrwvyXuTfFvNs3vre2+tx/7oem5+P6VBOKnm31X158fX9N1Rz+1/TvKvk/xeykTNNRmVsXOSHKnffVaSr6Y4Wj/vO+oxPDPJx1PKxZ+nnPvT67n6WP3do1LK0aGa1u48fneSK+vnPyLJ65M8sX7/STW9B1LK80Pq8d2WcmF0pObBCSll81NJ3lrP5Y/U/PpK/Xl3TGfV8/jBek7urJ/xuZQy/Ef1fB1Xf/bgesx3pTTun6rvf0xK+bm65ufTa1pPrun8WP39O5JcWtP64Jovn0gpv11Zf3dK2bgg5bw+oH7Hk1PqxG8m+Vv1WL5S8++emhcfr8f04ZTze09N7+NqPt2c0m58c33f61PKybfU4/5Qkocl+WI9tptSJtW7FagfTgmE/70k/2dKObm0puvElLL4jSll9pyUsvjEevyfr3nxrnq8D0s5l+9Oqc/HpZTDW1LOZ+pn/kVKeflCfc/ba779dJJ/l1JO/yjJ19bvHz9n59fPu73m/XfVY3hISt28K6XONkmuSPK0lLbm7PqaC1PO+8NT2tzP1Tx+TZLvTJnQ/PWaju9P8pKU83l8Sr24JqWMn1A/4xMpZe+jKe3vbUmuS/J/JPmVlLLzrnqevqHm4b01Xz6X5Nokj6359YmUc/2RlLL0Z0m+PqUsXla/88k1/87a8pnvqHn5hJRz/bmUc31+fW3q556TUu+6Nqnrn86un/eJJE9Jqf9dW3hH/f3HkvyzlPbkhpqXD6zveVo93kMpde5pKWX13oz6hu+o6TihfvZjx9J5c/3eq+vxXpjSFj4spV06UD/rzpqnt6Wc969N8u+T/C8pfdOBeo5Oqp/30Zo3d6T0TcfXPOjcWV/7pSTvrJ/x9JR6fm1NS1Lam0vqew+ltImfqOfpvUnelDIB9bqU9vG8evx3pkyAvz7Jj6fUk0fWfO/K7e0pA66vppTxAzW/T0g5lzen1LUHZ9RWvyGlPr8opR49KMkfJPmelAn0Lj8enmPb6K7//EL9rDfW49max5+p73lQTfP5NS9PrXn4yJQ27wkp7c4nk9xY8/gzSQ6ntCtdu/mllHrx2iT/JslvpUwCP3DsO6+uefnXMhqEdmOpW1PalRtq/j46pf39i5T6/sKUc5x63l5UP+OD9TsekNK2PSul3H2q5uPpNX8+XI/pgnquzktpYz+TUf35fM2Lrn2/uubZp1LKRptS1q6vaX94TdPTx/LvQEq7/JNJ/m1KW/O8lH7trPq+J6aU74/W47w3x44Ffj5lPHK4ft5dNe+/oab9eSnl6+MpZedl9ecPqp93ZU3bb6bU5TentCvn1Px8Zs3TByb5QEodOL5+VjeZ/JmaplNrHp+bUoYeWPPy3fU73p7kxfXnqcd4SkpbekJG/f6RlPHBryT5mZS68wv1s5uUgf9nM5rEPi2l3p6Z0sedmVKPbksZo3Tj3G9JOa9/keTmtm3/adM0V9R8u6m+/p6U8fAFKe3Rv09pf96WcrFxfErb2dWDbizajcvOTfKfUiaVbktps86q5+X4lDJwTUp7/gP1e/4wpRweX4/jnpS6f2dKufu9lDb0hpRy96tJ/ntNx7k1r95UP+thKWXvQyltzffWz35wSvtxbcr5vbnm5Wdr/nV91oMzKkOPrN//0Xos59ZzcHZK2/Y3Us7lB1PqwSfr33+tntOmns8b69/H1eN7UEr/fHc9xs/W951Z8/Vg/cynJXlPylisy+fxdub/TinDx6eUo+9PqRefr5/3hylBl+vqsdxRv/Nd9dydUj/zwzW/Pl3T9/CUPvjLY5/1gPrav6j5fXtKGX9Azd8P1XT/05Txwk8n+dF6Pk6sn/OwjMbcX6n5c3L93Xi/c3tKG/L5+plfqL8/McUn68/PrGm/IOWc/4P63VfU13V1939L8o/r/9ux9NyT0i5/rH7H2TV/PpJSJrrz+nW5f/tzdc2Hm+vPT6v58YiMysgNKWPdb0gpJ3eltBXn1fefUvPkkzUt3WT9o2qavlyP+7yaJ1fV83JTPeZ3Z3SdelJKHX1YzdML62d9LqPy9bCavi6ofGXNpydl1Pf+7yljhUeltEnn1fd9oX7XvfVYb6jHeF19/4NS6sh7a7q+LaUu/X59/1NSzvmZKf31e1KCEZ9JKbc31nx4VEbzA3fW83F2Stntxhefq+k9ux7rmSn14UNJ/lvKvMND63n5q5Ty+caU+nRHTec7UgKTn01pr34kpb58U0of9tP1vU9PuZ44tR7DyTVtJ9V/d8f0voyuQ+6uae2uYU7IaCzWXZudV7/n51Im/m5PKZ+XZXQt/7/W9J6b0p78TMr45NtTysuH69/n1+P9WD1HD89oDuDcjMbHd6boJve/kFKPPpDRJNeFKW3kE+rPH1iP7SX1M09JqaNfm1Ke25Q+5tkZTQp+th7bKfUcfb5+11kpdePMlDb88fU9X1P/vjOlvjylvu/0lPLVXZs/OaWf/uH6mV+tx31rRuXvlJR29lP1NR9OKQenZlTXu/64K9f31D8n1mPr+sBHptTBrXMA70+Zq3hvSp8yPma8J6U9flrK+f5Q/fP8+n1vqX+fl1J+mpRzfEVKObi+fu9TU67t3l1/9k31XHyuHvNn6nF+op7Dd2Z03XhOyvXbuSnl970p45Yv1999uObvl1LK8Z/Vc/HYlLJ7Wn3NyfWYjkspw6fW7/1SylzK/1WP7ep6DN9Tz++bUvr216WU9YeMndcHp5TRt6TUl9NTytHbUup+cuz4PzX/u5tRHpLSfnRj0q/UdJ2ccv4/nnLeu7mnGzIqw+fX77235u8jan78akp5uC2ljHVjw7fXz/h8/Yzuuv+m+p3PrufngTVNL0/yO/V8vDulHp+UUma/MWWc/69rGt9W8+Rv1GP7q5q3F9bvfk3K2PcJKW3jk2seX5vRdcuZ9fi6uYazx/Lsiyn17cdrvl+f0o7995R+8mdT5jgOpNTha1KuR74tpc3+5prXT6n59cWU8viOjM7zGSnlohvnPiCj67LPJvnTeo4+n1JHXpXSD78lpS49pR7nK+rnvillDuMtKePHz9Y8f0RNX9dXnlO/64aatpNS6sTnUurfbTV/Plnz4M76/6+mtCOn1rzvAh4HUtr/f5XSB3xbShk4uebbA+o5uTulPTq1HtdfZtSOn5Fy7XBuRvOQ3fXQ1RmNq7qyfVtN3zNq3p6UUk+3jvv/KOV8f3s97q+k1JeP13S9NaWteWPKdd69Na1vqufmafU9704ptz9Qj+PWeq6O1rSfWF9/QUob+Vv1XP1a/b73pIwb7kkpg9318z01Pw7Wv2/LaI74J+t5eXbNx1tTytCTcmwc4O56bFfW7z8vpcy/L6M51zOT/G7KePbnU9rD01LaonPG0v25+tmvSym/D6tpPVrTcnpKeXh3/fPC+jnd3PjxKWX5sfXvz9Zj+kDKmPQXk/zHmr8vrek/vubf3RmNW/4qo3pyXEqd+GhKufhKSnvdxQi6a4/j63H8SUpb9baUseNnUtqhu1PK5AkpDmQUi7io5u9tKddL/7Dm5zNS2v2b6vGcmtF47LaU+vXBmodn1c87K6Vsvbem+4yUcnhhSvm8O6Wf/UxNy+NSysJDU+pVaj79WEobcGH93oem1IdH1M/+zykBzcPZxbK2B3x1SuLenOSXUwr54ZSKeMvY/69OOXkfTzkBb08pSJ9IOWkfSKmMV6Zk0pfr65JS4D+SUmm/kHJCbk4pgPemFJpugHRaSud6akqjdWvKSX10SmfwpLH3dT8/s77uASkF5ab690+ndB5vTWlof60e07kpFeztKQ3+36+vT5JX1n//VUoh+URKw/iemk/fUtNxV0YBrV+uv7swpUK8rab9t8c+981Jbmjb9j+lFKRHpVyQ/n79nK9LqRivS2k0LkipGJ+saT85pYJdm9GEwRvq6x+e0QRZ6uuPpFwU/mI91nNqPpyWcqfn16ZUrgenlLW2vq7Ln9SfH0kp5L9av+valMmlO+rPX11/3uXdLfVYP15/1p2rB6eUk2/KqHycUH9/Tn3Pg8Ze39a8vaEe+2UplfHO+h3/LKPJtA/XdL6xnpuvppSvn0/yxykNz+NSLuJvTXJ927YvGjs3L0xpIJ9c0/bGJMe1bfu/pjQaX0kZWL4wpeF/SErHfHVN8yNS7oL8wlja3pNROTghZRD+gykNxRdTzu9xY8d0c/3zgpofv5lSp/4spdF4eEp5vCmlLh1IqaO31/8fyegcPqgez0NSytbNKeXy1Sll4MqUQXeX1g/VNN6aUiavqfnymCT/c9u2/zajQdg7Ujqcd9b8v6X+eU89jrtqPj8wpUx25/fdKXX6z+vvu3bjl+oxfkvKBewbapq7CdAun56X0lm8NqMgx+tTLqYfXdNyez3+bnLvrSmDsL+sx/bBmk9dmb43pdF/S827U1I63yvqubu3pv1P6nl6fv2+u1LuFvm3KYPfl9Q03ZXRRPjWc/Y7NW+7vB+v21/JqM5em1H9OLWeg69Paas+k9HExrNSyvSv1bzr0tDWz35r/aybU+rn+zJq029KafO6dvJ36/k8NaWT/0RKR//QlInEe1Lq53U1T05OqQtXZXQh9M76/5vrOXh4PaYbalqbJP9hm888mDLAeFtKuXh7TUf3maeklIGfrund2j/dW7/zppQBwJNTBr2fG0vPn9bzdkHNhzem1I1za56/MHUlWcr5/kj9nq5veF9GA5BzU9rU30s5tydmVN+6Nu3B9XO6NqKbhDg1yXm13JxSz9UfpgzmHpDSxp2UUk4flzKI/f6Ui7YrU9qNt9S0vCalHDy8noOL6vd/oX7/X9b8/W8p/eRTU/qn7tx/qqbrjfXzHpvRAKvr238g5YLp0fV4urp+ckq9eX/Kee3K+DtTzvcvp9TTk1PO7d0ZBekvTLnYefDY+6+t3zueHzduaaPfXD/7L1PKztNSys7WPO7qRZfmrk24rOZfF8D8nZqnt6a0bR+r778pZfB5XcpE7nNT6v3JKef44Sn1thnLp6T0fW9MabM+kVJmukDQM2r+35zS1nUX9F2b1pWvX69pfH/9nG4yoDs33UXVO+tnvSGlrB+fMtA/qb7munpsX065mLmzpvnv19fdkVG7+pcp7dITUsraM1MuqLvy1LURN9Tj/HJKv/Sp+pqkTGRdnFIeH5zSH56WUlbelNFYoBuPfDal/b2uHmeX9nemlO+uXnU/7/qUx6Sc34fVdJxY86FN6TO+mFH56PrCW1Pawt9NqSf/T8pFVneOfqPmR9ced8f/AymTSF2/9bGMJp5uTu33a12+pObRVSnl8W+ljAd+NuUi8BvqOfxM/exujHRqSr04qX7vP0wpt4dqvr+9Hs/dTdO8MaWe/NOUC8Vn1Xx/aEbt0U1JntS27Ttqfl2dcm6/mDIWvSylbTmcMr74UMqkyJk1j09LGY++rp6btn7Xi1L60atSyuzXprQz59Rz1OXnvRmV6SfUfHlJSr357ZQ+9J31mK+u5+S36md9Vz0H70sp/2+raf9g/dm1Y/n34JQy8IaU8tLVga6sfrS+//yU9uYNKeOB30nyEzV935xSX7ux0Un1/d14rhvndf1zV9c/nVKXu3z9s5T27j+nBN//uOZzN7bq2pmbUvrxT2Q0Kf5LKeXq1Jrm38ho4vXr6znpghhfqt/5BxmNkbv68MGU892l7cv1teeltF8Xpa7GSBnLHFff342d2pQ6d3xG7cWn6nF1bX43Rtra7zw3Zax2MKU8dOWiqzfdz69Mafduq/nTffe3ZjQJ880p5eUR9Tx246431vN4Y823m1LKRDc2+A81Dd9Y82y8/XlzRmP47mL/H9Vj6K55v5oSGP63Ne+uS7l558p6fn47pbx21yv3ZqyfShlDdPlydUo7e0PN55Pr9zw6o6DDyRlNunRjht9Jaae6c/iljG4+vDWl3DYp48ruOvFnUsZSj67nuDuH19Q8/f2UuvQv6zl9an3v76bUw4MpEyJHatqfW/PulnoeP5DippS6cENKPby95sOncuz8wK0Zjbu68UVXn16bUo678f6DUtrYLrDVtU8fS5msfWdK3evGy+9P8q/atv2VJEfrtVl3bdX1Kz+RMv7rxlAfrvnZld2uD/t8/dOluSvjP5PR+Gb8evPm+l1/N6Udf0SSk7dcy787o2u4b06Z8H5uRn3CXTVd7045t7+fUu6uq+Ocbu7i0ynloKnndXyM9Zb63bfV8/XVlPL35/V7Tk65HjhS87qro5/IqL89LqWteHR93b9KqUsfSWl3jq/n9C0Z9U8/ltI+X5LS5l1Z0/G1KXXqt2qab6x5fkNG1+/dZ76jfv/bMmqf3lLTcCClTt1bz1sXTL6mnt+DNb/eklFb8IiMbqB8UkZt4DFzALVO31mPe+uYsRsTvrVt2xfXz/3Omsb/kdG1yUfr+38mpVy+sH73oZSx3S/V7+3GUN18TZNSD69JqS8HU1Z1jV83dtdvD6z50gXD31Dz8p01/W+tx/edKePRH81oEvaTY8fUzTt01/CfT/J36jnuztuTUsZk76vn5Iu1Pp1Wj7c7r5fXz+nmm7qxQXcdvHX8f3N9XVLaj268f2ZKeXh/zcffrcfVXR90dXO8DD8tZaL5O1P6wo/Wz/vWmucvruehmxPr8q3L++76sitPb00JJnRt2p/WtHdtYNcuvyGlT3lKRvNHj0lZ7Xpl/f2hmu5uXNrNDV2Z0h50bVfXV3T1qCtPt9Wfd9dyXX37mZT2/CfqOTq55sPJKe3bPSnl5LL6msdm1ObdllJeumuvrt38ZEbXvLfVY/9kSv37i/rd708ZK3y2nourUs7vJSltyC01/05JqY9duWnqueo+oxvfXV1f94mMrmNuzmhs0M1lnFq/9y9T2onuGu5zNR0Pra+5pP67m+98Tkpde2NKufhA/Z5zUsr76zO6QfHPa5o+VPP8qyll5eUp5/nMlD7gxpQ+7jn1vV07e1LKGKtLwxcyqsNbx/3jc1gfrufhT1OuL56dEvA7LqM55i/XPH1SSnvc9buPSQmiXlvz6mjKvMN19X3duezms96Qcr38Uylj3n+YUp66unh2RmXxC/X4frfmS9d3dtfLV6WU+W5+7mdybBzgD1PO78frufqdlLrbXeOelNLXvL1+VzfGfmhG5aFLd3ct1c2pd+P601PGir9W87Cro934/3Mp5/gTKfP0N9R87s7Vt6WUv5NT+pZfy2gs0I0pP5TRzezPy6iefDZlbHJ3PcauD+3mYR6c0md010NPSylH35lybh+aUu7emmPr+Hgs4j/V/D9SX/vZ+t5uDrDrC7v24s9T6urH6/HdWs/nZ+vrP5VSD65Pafufl3JteENK+b4tpT51cwPdGOSemk/dnGH3vRe0bfvNNb1fbNv2R1PqxbdkD0tZaZUkTdOcknLRfG2S72jb9hfrz78pZZuQX2ia5sKUyvbrGTX2H00ZvN3cNM0zU07sZ+vH/kpKxtyVcuKfmDIZ/LCUCY7rM5ok+4WUwdPbUzLyjJQK/ZmUu23vTvK6tm3vbZrmefV3qWl4XcqJ/3x937enFOCknLA7Mro78+kpFfLilML32ozusL8lpQH9WJI72ra9u2mah6VM4l6ZMsB4Wn3tT7Zt+1+bpnlcSuF4f0pBOCulANxbj/e6ejxfk9Kwnti27Q1N0zwrZZD43RlFsq9O6WTfW4/z5fW9b2nb9vamab4xpcF9a03zwZRCeFdKxf+bSf5rzatn1u87pW3bm+q5PDelMXxvyjLCm5qmOVDPza01bx6b0iE+JaM70p5Rj+NDKQX33SmD9IM1L8/JaHXRi+tn/HL9+aNTKtm7UgZCX0qprEdTGqY2yT+v3393PSe/Us9dW9N6dspS/P9aj+O0lADfifV1f17P27Vt297VNM1DUhr8K1LK4jkpjdsLUyrznSnl989SOsuvSSn3J6aUt5enDMI+3bbtXzRN0wWu7q1p/KmMVgp+Zz2Ot7Zt+86maZ6W0rmcmNIBvqYe08n1GF+Z0gk/KOWi+Pa2bdumaU6t+fzZlDJ8TcrdKw9PaSC/UvPszIwCgP+1bdvPNk3zPfV4freew8fU8/Kl+r5vrt/74ZQB0f/atu1v1Dr/DTWtp6RMLl6Y0d0pp6U0ZHe2bXtt0zTfnNHd6+fV73tsShn8eE3D30gps69Jacifm9JpfCalk/mhejy/m7KU9uamaR5bj+v6JPfWsn5RTfvZKQ3vF1Lq7D1t276vaZoXJflq27ZvrmXiO1LqwIdTBrj31nJxT03jS+t5f2hK2/HuepwH6u//OOXutb9R8+1HU+5M+9V6Pr6YUo6uT5kcuLOm/81N07y4bdvXN03zwFoWTquv+ZOU+nBGSll+YJL/Xs/ZKSnl8S0Z3eF9Xko78ncyWgn2uZROqEmpXy9Jae8em9IB35xS925v2/a2pmnOThlsnTt2Tp+Z5Ejbth+tefVN9Rx+suZBUtqg97Vt+1dN05xVz917atk8VD/zdSll5TEpbeVtNc8+Vs/TK5P8vylt2QkpdeMNtb07K8lZbdveUNOw9TN/KKV+vD1lefT7tvQ3x9Xz/9iUCaGfb9v2Z+uxnJ5Sl5+bMkA6kHLX0H9KqVMfT/KBtm0/1zTN97Zt+xs1DS9L8pF6zI9MaZs/WNP6pJqWJ+bYQMjdNZ2X1WM8MaXOdQGCB9Vz/biUMn9jyp38d2R09/R5NW3/S8oEZOp5eHnbtq+px31JymCsC8o9MMl/SRk0XlnP6ce6czfWP7+gHu8LU/qiWzKaVPm1lL70SSkXmE0tI3+QURt6RkYrhD+a2n/Usv2MlDbvozU9T0kZ9HZ1/t6anisyWmL/yfq7R6XUvW6y6qH1c34gZRL+rpQ77N9f8++6JN/dtu0vN01zZkZ97Mn1My9OqSO3p7QN43n84frZ3QX4e1P6izalP3tufc8ZKRP9F7Rt+57abzytbds/rmXgBzNaefP4+vNTU9rM01Lq1sPqd30hpa4fn1JPn1nT810pFxTPqWl9eE3DiSn9/wfqz380o7uNX9+27dW1jjwxpaw9KaVOvz5lrPPKlLbjAylt56Upq6mfUs/BWzK6C+7elH7lCTUPrkwpn+/J6CLytpQ2vwvi313T+IKMgnIPq6/7dD22L6bcef6mer7PSBnDHUopfzelrPz5hZQLnUel9NE31fbvlJofj6t59JSUOvXGeoxPH/v5w5JcVsdFfyel3H0hpdw8sqb3OzJa8fmVlLHUY+t5fHNKPf6tWsefVdPTBauurN/3F/VY7smo335vSvt0d83X8zNaafS2tm3f0TTNs9u2fXvTNI+p5/XUtm0/3zTNM1IupF5az8fvp7RlD0sZ5zwopa69IaW9bFMmy/5ZzYs/S/KEtm1/KUmapnlaLavHp/QTRzJaBXJi27Zdm5BaZ+/J6E7XJ6TUzZe1bXt5fc0zU/q0ezK6+eumWhY+lDLe/Xjtpy9IaXOeVPPg5iTnt237B03TPCelXF+QcmFzS0o/fEPbtv9H0zQXtG37kVqXz6nn4DEpbXU3kXpxRv3W6+vPDmZ0h+gP1bR1r317Rqvx/ltKHXhNSrvzd+s5f2bNjt/NaLXB96e0o6en1L0H1vPwO/WcPzKlz722nufrc+zNP19OqScX1nQ9MaXcfG09d89KGb91Y6szkvxe27bddVFqW/2vax4/MqMJjnfX7/mWeuy/mXIB/t8zurP2XUme2rbte+pn/Z2axjeltEv3pLR3V7Vt+6k6NnprSvk9r23ba5qmeWqSu2s/+y0ZTYq8r57bezJa+XNrRnedPrH+rMmo3zk5pe49NKV+3FU/oxvvnVC//10p7WCTMl5u63ffUc/De1Pa4hubpvmXKeXxxCTvb9v2f6916TEpdfXZNb2vSbm57GNN0zy8bduP13bzlJp/j67n+sKMVhA8sbblz6t5e07KROhHM7rJ5efqeOGZKWPSd9W8fmnNq2TUT31rTdP5GV0Xv76+5skpbdxl9bz+aEYTgOdmtEKrTalXXd91T03z7fUzv1TP5QNTyuS9KfX+/JQx2BtSyt9pKW3CT6a0j9enTGa9vta37hqmq6+31LJydkrQ6PSMxp2fSXKgbdtP1WNJ7SNfkNL+fjGl/n5H27a/WNP27bUMPKO+5gUZ3Sx5S0pdu7PmxXtS2vY2pd3+2pQ68Kya9kckeW3btvfU/uLr27a9oqbj69q2fVO9djgpZfx7U9M0Z6S0y2ek9P0PqsfyvJqXf5HShx1Iua64s5aBNyf5aC2T35TkYNu2v1a/67T6/rekTFpdn9Iv3FHH3M+q5+0rKeXuJ1LGYP8tpW36s9TVgLVMPTClHn+hnovnjo37vj1lLPaSmi/3pPSx35XSrx5J6VtvTFnt/DUp9edp9fw9OqWOvDx1cr5t23/fNM1DU+plN0a7p+bvJW3b/lpN09fXz78npe16Z/3sizIKCnxHSnnsJrnen9ENBU+sefyYesynp5TVboL+iSll+7kpN6527duf1HN4R9u2H655/oKU+vDalLLzf9TXPbRt27fVtuDRbdu+pr7+eSltxZvqGOEhKfMhV7Rt+4Gmab6mpvPRNd+/kNLffqb+/GtrMDRN03RB1htqun+onverUlZN/3mS32/b9r1N0/xZ/azX1zy4KuUa9Yv1GvU767k6JaVefSil7fvLsevGR9Z8a2ve3pPRRGZby8qjU/qyd6SMIb6+bdvfbZrm0fX8vLke/1+rn9Fdnzwkpc88JaXt+VzK+PmFKWXq51Larhvbtr2mHv931PceyWhO7DtS6u5x9TP+7/q919TPuaem9Ssp/cW99d/X1c96VMr49tk1zx9SP/+m+tqnZTT31JXhJ6bUo2ekjEfeUn9/WS0XP5dyM82Xahk5PaXNvCGlXpyU0ZzhC5Jc3LbtzzVN85Dalj5m7Nr061L6r7dmdENZd+PYQ1LnQVLapodntKr4/Sl90KF6fg7XvPpqypj3KXW8dmHKOPdNKeXpuSlt9gtS2sEvptS3Z9cy88kkf1bnGM9q2/a2em7OTun731DLzccz2kHi3oxuhm7qGOuUlLbrlIyuTZ6XMtb57pSb19+UUn5/P6WtPj1lvuDuOgfZ1PPxKzVPfzRl7HV8Stl+bP3/l2oZODulj3xBSrt+S8q80xX1/efX73xJSjvajZGeUfPsE/X3Z2a0EvXClLJ2cpK76rjlFSnl7pX1PJyYUlY/mFHQ8q6UsdOnMxr7vTClHj6gpvPyej4vqOftf6RcK//3lHbwl+s57fLvhIzGvd24/7EZrYy5N6XfuafmyVMz6uvuSRlbnp7Stn5NSl/0bSn9+ZUpZe5LGe3y8Icp7cHRlLbm72V0o8w3j/2+qfNR/3gsX5t63s5PKRtJCaodTblO+kz9vo+kBKJPTLlmuzXJw8fmjF6cEgf4xTqGf0w9719T39O1Vaek1NN31zw9ruZ3FzC5I6U8/LWa7ifXz/pKPQ9n1jw8Yyz/uvP5dRntdPNjKeX1j1Pq3ltS6sLt9Vx9MqWdvbeOe56SUkaO1s8+J2WMfVdGO1l9OKN68smUcdldKddu16SU8TMyWhl6YsqY4e56/fPy+r4n1eM5t56v/5hyvfLReg5Or3lwa9u2n6v5+9R6nP8kpc18dj0nv5Bj50wfXNP6mJTrubtrHv15zZ+HpLRB31SP7ztS5jh+N6UOtBktXLmgfu4jax58W82Dx6XM+f7vTdM8uR7/Y1L8Xtu2r80ulrU94LelVOAfTCnkp6Z0wB9OOZjjUgr0SSkn/aaUzPqhlIz+vZTB94+kVPq/SFlqd0NG26c9M+VkPCalMDwmozvJrq/f/8GUCn57Rks4u8HR42t6uqDW8zNatv+GlM788ykd3T0plej5KSfzMSkn59kpg6nvrGk7VD/z6pqW700pWCekNDzvzmi7vDNSJvfOzOhOroMpBeualEHOs2v6ugvcM+rnH6qvuzOlsHxPyp2Bb0hpmN6X0mmflNEKo7tTGuszUyrbQzLamuuhKZXu9TXPTspo+4XTM9re7QMpleUjNX0fTekMnpgSae7y6lH1vD0mZdD17HoOjmZ0N8BX6jnoBrs3plTQLpL9xZQy0N3d8uKUi7n3pXQQt6QMRp6S0lAenzIp23Usj6rpfXFNS5vSuD8zJbD0zJRzfVpK+ftqzd9H1e+9IaPtJ+6t5+jtKZ3ybSkd9UtqGl6bcjFxfEoZ+mj9c29KQ3RnSsf5nSkrk85JqROfSGmY3pZSts7K6AKmm1C4I6OVX3+a0eDrpIwGy12g5Mx6bq6ux/K19Vx3wdw7kjyvbdsXNU3zv7Vt+781TfMjKQOnx2d0B9sj67F3gZhukPDRmrddQDQp5eqXUwJiz8xogvK8mv+PTKkL52a0Jc9d9b23ZLSN4ZcymlR7YT0fJ6UMvL6a0pl9qR7jL6XU64ellKOLc2z5en9KO3NbPbbn1zR9seb751LK9OdTysLTM9ry4/SMttF4a0p5uDelA/tcfc3jUy5gP5ZS5765fnbX5pya0gHcnlGA7ZH1WF6U0d3bf5bSoH8kpdO9KaXN/O6aJ0399/Gpq/lSyvgz6nd9qf59Ukr5fkBN6+31Z92dQI+s6bgxpRy/O6UtuKem+/E1j/80pbx+MaN9i4+r5+kZNR1/UD/vySl303RBr3Nrer4/x7YPp9W8PK3mw5tTBjzPTGlTT88oqHFKfe9DUiakurr5lIzq9t/OaMXXk2setvVc/vX6mg9kFPQ4N6UuPDSjLVBPSSmfD6h5cVrNn3fU9zy/HsuFNY13ptS7m1LK7iUpbcHfSemz2pSVEJ+oeXZz6oR6RpPhL6/p6NqHO+v//3NKu3Bazeuv1M+7q6bxxJTzflxGA7gumPWM+vP31897Tkob/pKMtn29OaWN/nJGNwE8pX7+cSnl9fMZbZX1/JSyeUs9F/fWc3Vy/eyXZXSjw9fX9P1JykXTK5P8o7Zt/13TNL9V093dqfTgmn93pLQt16fUk26C+fP1e45kdAPJ8+u5fF5G24J+JKX8HcxoS7LPplzQXFvz7G0pdfWZKQPZH6zf8eF6POePHX83Hjgtoy1nP5fSbz2xvu7EjO5q7u5SfXZKH3FqRs/ju6keWxeg//Oa9q7+31T/nFZfdyQlQPf/ZNRuvqD+++SMzvsDUtq6D9f8eEc9tl9JucB/bv35MzO64L6zvu9ASvt4oH7veSll/vdT7vS8J6XMvbUew5NTLqzenlKeP1OPu5sofklKXboj5YLovJTJ4WdltMXBaSl93F0p9fgx9TgeXI/hXTl2jHEkZbzympT6fXLKRfD31Lz9i3qur00p98/PqAw+PaXsnJBS5h9e33N1/exkNN67seb/ZzJqb/9JSj/RpATIr6n5dlpGd+m9I6W/6SZyPlNf87T6+afX4/urHNv2NzWvUo/7hBxbr7p2vDu3B1La0t/OaFeEH0mprxdm1Ede17btd9eJ+M/WvH9C/Yxu7Pj8el6elNIvvrfmyd+sef+alPbk3TW/f6KmuRt7HKmf8y9St3BomuYf1Pe+MOXcdjccHZdyYf7bbdt+eux1X1/P05PqMSejVSDduKjLn+tr/p6S0XPHHpHSfp089vefptTfp9fz8JyU8/j5lPb04xltVXIgpS14Qv392TWvP5PR3fn/IKWsJ+U8f29Km/BTNe1/r57Hv0xpG34lpa2/rub7vfV335fSHvxVykXl36vH8NWUuvml+povZXRRfnJqwKj+vLtOOq+m7ak5dix6IKPtnB6XMhb+ctu2/08dx31byuTIt9a8vDVldd5xNc/eU/PmISnt2FkZbRH2x/UzD2Y0ud2dm+tSyukJGW3D+Ij6ubfXfDmhHucD6nFdk3L+P5lS9rqJvzPqn/HjeGJK2f31lDLxipQ26YKUctaNzf8iZZxwZUo5eUxKu/pHKcGpf9I0zc/Uc/C4em6Otm37H5um+YcpZeO7Mho735lRQKerR2eklL/XpLQln6rH8z313PxwLeM/klLHPljz9on192enlK2nZBTYO1D/Pin1Rqma5m680o0LmprXZ9ZjviSj1SQ3ZLQVbNeXvafmx0k139ua9s+ljEPOT6l3t6W0gafW9/5pRuXm36eUve6mgltz7HXuE+t7uvb2W+v3dDcH/VnNn79f0393Snvzk1vy6R+l1NVn13N2TUpQ/F82TfOTGY3THpxRXX57Rv3nH6SU1/E8f2o9f1+tefyweg6emtLWX5lj5xXeXz/rG1N8LqPrxm9MGcvfPZauV9bPfGZGz8N+bz3HD6rf+Uc1vX87o5v/rk0Ze9/Ttu2/rp9zIKV8dQHch9d035HRFocfTqmvf7+e00emjPfOqb/7vpQ6eU/9//tq/pxU0/WolLp9Wj3Wx6W0jXem1PenZLQNdZvSzry9puXTKWP+t9f3n5UyD/Pi+tn3ptTFr6951x3neRlt5XptRitsH1Lz6556nGfX31+UUve6a7WHplw/d+OK21La04tq3jwkpa6/LuV69qb62tfXNH81pRxem9L+HUxpO/8ypd+5K6UNGr+GO5BSX56f0Q4/X6nnp3v9Z1Lq30k1HSekjJee07btbzdN80MpZfLvpkzmjY/Tz0gpf9217ZdTrqkelFFQ5dEpbc4PptSNi1Pq8/Pr+z9Z8/lpKXX4vSl1+lkpfeHrU64bvncsTa9s2/ayJNlS5h6f+19bnNy27bfWMUQ34fjgjMZ5j0rpm8avNbpx4Lkp44MTa7qeX8/bmSnj5iel9Au/n9K/jpfzbj7qnno8r0lp1z6fUoYeltFWp49MaTM+nNEY/OyMtiA8L6Mt7q+o7+/GMW39vt9OGdP+YcpqkQ/VY722HudTax5/IKMVSEfr8Z9cj+/JKW3knfX/F6SUn/+3vv8Z9VhOq5/TjbnemVLfz6zH/Z563I/LaC7y3oxudvvWjAI1r8uoTX9pSjs1XqY/k9ENjjemjO3ekfIIlV+v6bgzZfx4RUob1vUbXVv3vBw7HvtyPYbfyrFtf1duTknRppStL6W0T2fXPDk1pcw8tx5nNx/x+oza1J+q6fz3KX3rq1PGpHenXEd25+qfZ7T6tmtPxufVHpHS31yZ0m59IKUcnpNSP346pU0+P6NdFr6cMv7uxiDHpUy+n9K27U83TfPrKeX6byb5VNu239A0zT/N6FE0b0mp6/ek1N+uHT6S0ra+PWWiv5v7eHhK3e3ahCtrmr+QMhb6REbzDe9LqSs/VvP1ESnl7xEZlefTMlpt96CMtj5sM7q+OiWj1acvT2ljzkwZOx1KGa9fkNFKuNtS2p4X1s8+LqNrkO/PaK7rupT63AXb7snouufOmtY/TJkLu6vmWXft/o4cu4Xyo1Lqx5tT6sJJNd0npZSBb6rn649Syur5NY//OOXmj3/bNM13Z3Qzyvj8fBdT+KMcO5Z8fE3TE+p5PKV+3sNSysX3ZTQ/1AX631nf89KanutS6tl3pZTb7nrnpzLayeLxKeOJZ9XvPVDP1QdSysgnMrqJ4IsZxTSS0fayZ+fY9rCbVzqh5t+7a3o+lVIeu/HRU1LKzkX1969J2ZKzC9RdnNHc4/kZrZA7kNGKraYeywUZ7YzxlYy2AH9BSvtyW03jv0kp43+aUhf+Vko5vCGjHYs+nrG+KmU3kZ+p14kH2rb96exiWUGrn0/JkPemDHIuSCkoz8loouirKRn0+ZSDfFdKRjwo5cSfk9KgPC+loHQT4C9Nicz/bMoJ/i8pBfthKRcBv53SSHad6UkpE4M/nNLg31o/5/L6HU+v391VvCfW73tWRnvF353RxdMpKY3xpSl7wv7fKSfuLSmV5SE1LcfXf788o7tcn5xy8v9eyuT1e1MmpW/O6DkJ56ZUrPeldDRvz7HPMvpCzc8PZhQA/HI99r+WMsj83vr+/1y/5xtTOvlT6vGelnL3cTfhfGL9zqtTGqju+M+t+XliRvuqn5YSef1nKZ1sk9qYpDRaz6jvuyRl4umemjcvS2k4n5VSObqA3Ok1T05LaWi/pqbtWRntd3xc/cxfqN9zT0q5OCHlov76+n0PqOfq6vp9n0uZlPrmlAr0b2oa31CP6eSUCvqllEr/DSll44b6vV0H+rSavh+u+fXhetyPTJnceHLKwOGlKQ3Oy1PuEH9OPaZnt2374qZp/jBl4Puv6+eNT859PKML19+s5/EhKQ3ka1IahW+px3hlTfPBFB8bO/ZktGXXU+r3dA37+5Jc2Lbt9zVN8+6UOz++tr7+76c0SB9MKScvTymb/66m66H1Zz9e8/0r9fgem7J66MU1Tb+VUuYeWvPqixlt5/Ot9Xi6sn92Sjl4X0o78MKUstq95hkpd/u/JKUh/u2UBvbrUhrx19X3vzHHlq+vpJSZJqN9xJ9Zf/+ClM73pJQyc09KeTkxpf51Qc2zUvb3/1cpFwXf2DTNv0gpRz+bcldWN+C8O2Ww/uL63uekXMy/LKUD/rl6Lq/P6BkwF6QMnC9I6dD/R/35R1IGBi9OGWyeXfP0uRltw9HW37++/v3mem7+uObVn9U0HV/T8vP1Mx+c0cXVxfX1z6r/f15Gd7LcmtKpXZZSrn42pQyekNKxX5jSHvxQyoT7zSlt1PE1bYdTLsTektKevyijLRofkXK+L67pfmE9T7eltPuvr+f0L2q+v6hpmj+px/UHKe3zc1MClwdS2uFfymgLksdn1GYfV8/ve1Lq81Pref2DjCZnn1Jf/x/q8Zxez+FJ9e9D9b2/W7/7RSnn/IdT2tuvT+kDfiLlouin67F2AbMnpNTBt6Q+ZLz++231s9qMArufT6mrL6r5fErNp6Yew70Z9aFfTRkU/6uUfu3/rO/5+pT27vtrOl+eMinQ1Pw6N6Xe35PSjjw/pc19U0bB4q5fPrXmxx+mtBNX1uP5g4wCxV+fcoHx4xnd2Xxzkh9s2/aJTdP8Tk33cfUY351Sxz9c8/aLKU7IaEK3GxR3geuPZ7Sq6sX188/IaAuYWzPaK/pPUgJBfy+lTbs2pY3qAnPPSSlbd6f0+S/KaHuSd9V0dBdd3aD1xvq6S/L/b+/Mo76qr3P/OUwiODEqDkFU1DjggEogzE4Rp6pVkzaxJr0ZamOiUdOoiSFijbFtaprhGq3GpjaaxGicEBCBiKg4IEijJiIC4hAUBJkROPeP59nsc368eHvXuiv+c/ZaLnzf9wz7u4dnD+c7yE5OQsnteD/vEfM8lIyZBbLj11HCPcK8vIHylzX++2+QDd9i2UQecoifERM2HvKzXzMfg5Cf9SVX2o22vm/DW0eVZXm6GxO7oWT6VBR/l5NnLxztew5Hfv8ny+4mhFtLyHOG+iKsGk7O7nufPK9mb2QDHSybtWR8foD0idlkjjEY2dQ9fvch5DlnZyNfWY8wbK2fc6F5HkqukO5h3W5CtrU78tujrNsuyBZ6W+67WravekwXo5jzCCpupyOfiNyrsAzmW57PkGf1xIfsKvZPJM+Ti8kYVb+aQF238ZF5vuX/XWQj+yFfOAcVDGvIVY8RS272M6Jo6k6uzj4N2co6FN8/YVz9lq/ZYJmdbJ7aI//eiLDsuLIszymKYiyKiRGfd0Y5wk/I80N6I5+M6441jy/7eR2RvQxH/tMJ4VdBznqMIm6D3/8DZIt7AtM9Y28isof/Is+IG47ssReK8SA/iWZXb9Qc6oZ8LD6w/j3Cr/dQATgXrdbsTK7QOsbXzgD6lmV5nj+Q9DHvi8lVQd9BsaOrnxHFYQ/r8Fdk7FtLNm139jNettz6+v8jF53t8X0c2cgFwJVlWX6nKIorUAz4iXX3EPLxA83fZrKRui/ys6MQJi3xPREbI5fohPxhEnlGYkwg+KOfvwrFsN3I/fHbWY7dkO4HWj8bUQz9vccx0M+6E9nuAMvrMWT3E5CdTfe7B/n65eQ5K3sjTCyRPX0W2c3t5vVoZD8HW4dTUF10WkWn75MfyW4BhpZleZZl2gnZ8lfM4xjkd1EPriZ3EJjg338S1ZcftT5+aj0/gmJLV4RL+1g/LyB8O9Kyfot6nnGb5bfeejzY4zjW8rgL4chEhIe/RrFgd+tjpXU9D/npd6jbTS/SBzf5PVHnTve9P7J+Nlt2o5GPtCfxI/xkuXVbldMB/v1yhMEDkD3fjGw/4u0gj62DZfU6wtR5ZNMmZL4Z2cpij3838iyLLpZ/ta+wF8qZpiEbn+yxfdU6io/8Bbmd5xsIX542TztZxqtRbfIly2AiiqU9zcNZ5OS9TeaxD7KB0PnHER6NQhgQ8f0cNCHqbo87Pgg85feH/k+wfPa3zqaYl0GWz4PI/zqQq4i7IN8/AuUbz6FG6Tnm5UnrcVBL7n0j8rGpyD4v8zgDX/bzOOeTk8k2oDr0OMv9MTLOPGo+f+Dr8dgO9zVPIjxfSK60vh/lL7cj2xlonfVA+dXDCEuu9jNm+t/OyIYit12IbOU51BO4y9etJVcU9LauNvq+jsivl5O5QvRANiJ/Kcx35JC7I7voSJ5dFTn8hR77XOSvnbzSbxTCztORr/Yiew2LEHYsIrervKbC0yXIJj9H3eamUa8txqNcvSt5Zvv+CGfaW67rSFxbh+z4CtTc7Y3s83jz9S3fC8LPwxB2LUeYWrXzacj3fmjZ7YBysb0RdrxoOUVPqEA+MA31SXZEeh1Drs7vRH6kehjZ1wNo9dgJRVFMAx5x3hB50qm+didk/0OQny8jd7bpQ9rCOdZl+NlmcuXKPb6uH8Lavtbf7R7DZMv6ewjf51t3xyEsPo6c9LS3f95E1tORN21CNnkc2St7zs+ej7BsZ5QX3Gp53IBs7UXL6haEdc/Tdj5WWBZdyNUt76FYE32GnyJ/HIT0Ptr6uhx9dA+fnEdOoGxnOXzOcnkP5fbLLbtNZVmeFLoiceJ6630n6n21R8jFDwst89ct/0UI7/dD/nWy5dWRjJ+zyO0Y21unZyMfOg/F5++iXkUXhF/3kli7FOUyU0jcf43cYeoUhEnDLLel5v23aIL3GpQvD0a56m5kc38OuQpvsvmf5PEOx6tqyDNKN5E4d5Cf/Rx5Hlp75IMLkY12p35OWV+y5vsRWYOc6XFEzfEH5BvxEehlZB9zUC4eNjoO9US2Q751he+5B9lRb+Qf95uXieZnLrnjySjUB/0IsvcvItuchGqB6Pkvswyino/eRU/quWT0mvui2BEfmbpZPu+VZXlhURSfRPHtBwhLA993QXjxb9bX31o31RznRZTTzbZe70J+0hvFyqUoD3oRxYvPmb81yBbnW64L/MwXff8iyyZqrOvILe6jRvsieRbrCmSz91hH8f1hsa+p2tVN1H35MPL828eQb0b/+mbL9QKy5/aMZb6PZRm57BJkaxej2uqP5Pns4/2e36CPVnP4APpzfbS6kjzYthv5saUfGtAeSLEFeZZDH/IA0T1RovXXaNAbyBUgu6Gi72zk5B3JA+qfRICwBAn8PlRUr0RGtBNS0MNIuevRMvHzyBVCUXCtNO87IeM+BRXIO6Ig8Rx5vtQ4ZOgdyJmRJ5EHoO5HrlJaggLwxsoWLQeX2lLqSvM1nNyz820EzFN87wZk2EuRwa5AhngUajxeihoUUYx903xHofoxy/5A0pHfJT+G3UKuwPpLctbxk753vWVyhvXwDCrkv0nOYnrF938NJQ+voiDWEzno/gjE1vvfPh7H4egj5Bw0S2IYSpJvQ051OAoMn/MWD1NQ8NrLzzrIcnvGz93Lzz3Z+jwO+LnB6R98/V6W304e67HIIQ/zve9Yj8ejrad6+efD/I6llmsU+Z8hPzJG4TURFdPnocTkeARQBbL5LghE+5j3Jyrv+7Xl2wc5/hmoKf0zZP/t/Pv+aFb0L8nDi29FtnMQak6sBpZ75ulA8/ePZENsMAoCnVCgeQHZTWdUIP3S43/Weozm+QLUsF/md2xnuRR+7niUZE5FdvAw8ql3kL++SK4SmEWuZDzKY9zTsv61/3YUOQtvHRmYz0C2stI/jyJXNkxHWPA0CuDPIfu6mjyE8W9QkjIS2e0zKCFb5/va+55paNvMdr5nAfLtDSjIvoYC8qf93vfI/eIvM7+DyW1pViJfnednjUAfP6712Bdaphssu53JBt8G1BychWgluU1g6Ws/jXB0BfLRf0A2Mw/5VDQr4u//SfrMuagQ3B7hdy/zNwP56pWoOL4D2dPXPN5dkS8ehPR7Agqgz5ArIHpatpvIDzWBT+/6mtVoJtQX7O9dLZtzUfEy1TJdSR7MfChKUqcgX/2uee1PFonnkyunOpiPHS3TSCA+Tu65PMbvfN3X3En61r3me7zfNRcleP8bJSlHosbINF9/CcKEkciG+6NY0wfh0AZkhz0RDtyLfO9vkU0/4bFhnawsy/KfvaXAGJSsdkEFw5O+53rkX8Msn/kokboPJacLLJNlKP5MBf6pLMu5sGXrw4G+riuV+BVkTF1guR2ICqZLEDZPMq+HWob7InvYg1yJtATZ9SKP/Wlkw0PJrbIWIgzpj2LubghHoqH0JIAL1JvIlSfr/Y6dyZXIByLdziI/GvT2ew5D2/GMQz68K7KlP1hPL1uGB1tuT6Bm6CfIWPIminWzyZmih1iOi8zXXOvgSv//68jmJ5q/c1FcfxBtH/pIoW1VY8zHoXg9xzIbiwqjpWgbqu96K59vm6/4YPgLy/X3CHvXIvvojDDhEnKG3zdQ/Ohn/fwc4fbpyO9nINwb6vcOsmy+jvKmnsimDyf3AB+C4uYB5Me6DR7T++Z1o/npSZ679yek+0+hYmqSdfd58vzGxQhnDiBnFvf0uLv7faGDPVDjZqX5+0eEu5FPriW3xfhfKKZusswfRb7V1z+fRcafHsgP3zXvT5A2drx5/wWytyEo9/oZssudkP09Rh6EGx8GXyVXKm9EtjUaYdY7KPeKgu19lIc+bJ7e9jjWImwaTH4oGEmu0n0O2dUCy2htWZbXeWuz0QjPeiKsmE3OGlyK9L6f3305wt1V5u1k5BN9kK295ucsQHFlkHl9xXxegnxiCvKRo8gPtLPJ7cKXo/iyzuN9HOn5NeSH633fO8jn7jc+fB7h5TsoPg5BWzjP8kzO0ahR/AlyFcZ6cmZyD4QD+yL//ay35fp7j+lHHs8MEo/f9zh/j5o/Q8hzFgah3OoBVCxvsIwHI7sfgmJBgeLFo2gru3FFUZyHqKv5vA/VSKNQPF5GzlJejuL0fyN/mU5uMTLAvBeW7aGWz0jrsgvKe45D/tEF+cN2KH+Y4nt+i+LyKhKbjjIPa8iz+EaTjcgNyLZiMsN6lDssRv6wP4oRb5JbVB3hMcwmJ6MNMS97opruawhP90J2Fv7SmdwW5yVkn/uRK4JXWt5jkM8uQZjyFML83/qd5yCb7ebnUZblOOuDoigOQx961iGM7Ibi+UFkc/gjqFHyBRKvnrG8RiDfPQHFn8iZOlsWhyK72d+yPpY8l28WwsaZ5GreGWgF2s2VGqr0NXsgm3kH4eyO5ucvkR2uQbYxGmHRt5DvrCEngUSOPgHh/7V+7hHkaptZyHYeQJi7jjx3NfL84xF+Fn7Xvv55vkXbhVwleTL5UeJT5MSk9zyGV5Hf/hH5yAF+/gT/7jyEySGH5R7zduSh7C9XVjXOME6ch5pAr6PYFfXc7uS5XW+Rzah2yI4OJbfFnIfi6QRkqxcgfz0RmOVZyV9ENv5xy/pIZEe7kL4a+BZ12RqypliLfH9HFFM6mOfX/ayNCIf2Qrj2FsLzc1DeOQk12Y5C/nIIeR7GWyiHvAvZXXdUi81DGPM4io1LkQ2sQnnTf5RleYX7HpM9tvhwtQLl2ceieLQv6mOsIs+0i8b4So9vNMqxl1vXlyL8WOfrOpH55WYy1/i2ed8D+Vt/X3Ocx90d+dcg8zUZYcydqIn5EuqbbPK/t5mnQ62bJcg3A9PWIBvrUZblNbRBzl3nIBuN/PBFcuXsw6jmPoycsLW5LMuLnIfvizClnf971+NYhnoRUetHDyDi2ovk5KQO5DnNy5B9tLNOZlgva8uy/CfzfC4ZCy9CvrOfn70PebbOK6i+eMNyHkM2/kejfPda5NNnkzi4EX3AmIDi49Noy/2TyG0j30P5UtQXc8jzjSNmT/cHwnP97uUovi72eKOeXIbs4zS/qze5PXpXv2MCeWZ7fAw83PI5xO8bgmqKH3pc6z2e6Jcssg5XkKtgelpWi5AdveB/B/jZjyC/bef71iJc3Mfj/in60PxNZLd7WUaXoZz935E/r0UN798hvGpP9lG7+tmno1z1JvNwELn67naUh+xCTpAe6HGd6J8PQL5ynd85H9lIe/Pxd373Eo/7BfNbkjsJfYZcObvS/+7gd9zv51xMTlBdQG5PP9D3RA/wBeo7zyy0DB5EsWdNqW3kD4uGuvH3deTP30f5W+jgKTI37IrsdjXCvrcqfd7ICy81L+vIoxwWIr/8mH93gX/3AOoxtUfYVngMJyC/GInsAcvr8+RZ5GuRPQ9G9j/Yv38P4faDKB95k9xm8BSEedeZlxEkTvZD9XQv8oiBfZFvtDNvC8gcdDeEm5d5rNE3Go56rB8hz3ssfO3msix/aHmd52edguzwJXQMzdecn3/K965AuPsR8ozcC1Efay2y+/jYtjuK5Tsif+9uPYHsoWNFh8s9hjUoXs22jF5B2DXO8p2P7Oxdv2+6x3OxZR19uOPQh+tXzdPRSMddzPvO5PnQTyO9/6wsy3+lQp5IcgPCzK6oHvoCiRmHozNH21d018PPPcq/641yhJ97zKci3OhhmUS92MN8TfL/j0YTd3uR/hffbLb3O1f73vllWX6v8NbL5v3C0O//lP5cH60uR4Y0Ahn2ZqSkWxHo9UBJaz8yke6PivahyHhjpkEnZDxzkaNth4A8nv0+udrhCaSUP6EkpUDC721++qOgMAUZ+0YUUDqhxCCK09NQUtAZGdYOCHQPRonNYv93NgLtUcgZXjJP0UzejjyIeihqQpxYluXI2J7N8roDge0oFAxW+bmfMa/tEPCub+v5ZVkOLopiMgqMtyJnKS2zr1ie73pcdyGg3t1j2I+ccf8sMsK3zcM+CKR6WM6tOvwJSprOJhsE71meU8kPSX2Rcz3oe3v6XSst/5nmYyAKQEdYDmtRQLvLPK20rieTX9SfRDMYulrOp5dlOdRLNteYz+V+bzTpZ1RkvdoyLszDXv55gGWNr+ta4W13srBaiADzLHLWStWOQt6LPNappfYE/xYZAB+wPA5GttuHDOA/QQ3vJ8hEcyfgWSf8V9kW5qKCp8r7Ruvwdrbevuti87s9OQOlk98fH0SqutkbzSr8AZoVNqrl3ZdaVzeSW94sQ/49BQWMfW0LVf95C4H8p1FS8JR5GIOSwDPIGW+jyVmTt6Og9SyyjU/4+qq93WWZ7k0WC1XMWGU++vnvMdaFKKhdhVcaVsYbNncC2QDsUeE5PnLuSjZ4h5CzX6o+PpXEoaGkL08gt727AM22vQxh5lVoBsUO6KPxGGQ/B3kcS5BPPoD8aQO5nHgFufXKiWRD5jHzGhhwSGXM45F/fduyudi6fhzZyJvkOQvtSbsPX9+NOt4ObeF9iuXzlPmdad4msLXPDvW7ZqAg+Sjy3xIF3XvakMd6lOxVbW8voH1ZlidapwWaVXMvdZv9F3LrjtkoGdqZnEkyj9zKYVzLuyIZ3Izw4udsbf8Fou8jm3qbnLUTsePOFvmGjcfYqj59h/kcSR2jAlcPRXhwk58RPIQPX4z86lyUCF3jMYSOHkOF9jeq8SuoKIpvkOcvdUaYtxuK5cOR/qdTx9nA3d0tzyjSBlCPBcsRFn6Z3O5mILLXgcgmRnu23lXeluerluvN5Lkhbcm2L3V86IP8IYrA58nZadU4H9e9guyim+VVsHV8nuD7f4ww9BrrsAN1PxpErthu69mhi2lAR8eSq5DdrEC41yqHO1BRuY5cxXINGTcj1zkS4VhPKn7idw0htzcJXU5CNr2PnzOenGBT9YENvuZX1HFmQEU+c1GRUfhdzyK/uNfvOh3ZZ0f//i7z2msbPAbO3O17H6BuV4dRzznP8jMftSzPKMtyuLcHaU8ll6rE3ZXUY35r/LnP746PMxutx3ep+1fYS0fquDsHxa7Iofcpy/JgvxvL4TcI26f5/kHUc84nkH8tQLnbZxGufZ16vI88YDB51tZANLu8NWfo7Hvj2smkzX3L8piLCt8q7vw7iuf3o0ZT2NoG6+LH1P1xGnmYdBUrv25dX0nibisexL/PkGdmTiVzo/Dd16nHyL7kR7tWHx6L4s0WDEQ+FbXJLaXO6or6Z3vq/hExMXLnL/HBttuqwz0RPn8SNbOvqPj4ZS18HUDmG7vbLi62DFeYh7bi7R7m68e0nWtFzv4ouTKvPyq6Xy7L8oJKLdKHrLF2Qni7L8rJnkBxohN5HsJm6vXPJdR99Wa29qfd/dyoq+Yi7GrFusjXwmdLlKcsJifPbJUrk7F/ZAtvESMjNz+aSp5YluXVfnfUdz3IPPc1ZLvftv6X+e/R3P2l5RLyiY/8j6IceAr1HGtX6vVi5Nr7oTylF7mbyJZc37ZSjZHRkO3vsVVzw1p9UIk7K7Zxf/hya505iK3z3PdpO8/fmXpO0Ms/v0w91w783J5clfQyaqacTjbnVlGv6VaSNvsQmvAR44iYFfVvxOPWfH6Ex/sssqOSeo5xN1kHHmy9VOvtAdRzkZh4cQ/Z45hWeeds0haXI9yq+uRocrVPVdZjMXaR27/Hx5eItb/x++71mBZb7xNI/wl9X0vWpNVcID5STPPPeyMbn+rx7Ubdt1e0jG0odT+s1mSHo7pjOFvH7ov9jCdRTKjWz4GzE9Aq48j7W+vmyEUDZ8+nUvOi+H5aWZbDiqJ42DoKjI9xRg4Q9n60ZXo3inkPkT2KrXLooIpfttYVEQtXUB/rPJQrV3sb0T+Jmjzi4mrqsT+wMPK38JPo2/2M3OYrcu2TjbeXVT5aRf5/DFmz/RX1ONuBen/uo9RtOngOmf+Wer0Tcf55FCujfzeeeg04qaKrSShethUbu6EJDbegSfLV+ngi9fgbNct2fndMJtmTejyLXmLY/CtWa9SqpyF7GOMVDFeRfYTwzcCRHajU5BV5RI5ZfU419/oywvDDqNd4Vd1EbhC16QHUselwhKc7Ij9qzVdDl0fQRp3oMW1H1hXzyJ5ptZYfguy4X0VOR1Cvue6j3j+4F8WE7h7nl6jX9CBMi15J5FLRWx7usfVE9vZXZE4ZWPdRZCdRkzxJG33tsiz3b8HXGPdGHGeh1hePvGgU9b7fPPIc7Rh/NQ5V5RG1fR8/Yx2yj4jTa1GeH73F0GU3P38DihUTqONVtX9e7Ve2Fes3UK9J1lLPQdf7nu7UcfWvUXyu1voPoHg2pCzL0y2vy8je2pn25efQR9fIz8M/om/djXrdvJp6jv049Xy3B5Vcmno/70xkNyche6v1ccndeoZQr1HXUcfVyNPmIZw5n3ptO7lFF1v1dqhQJTZEDN5sHjagvOtS1IefQdpu+GRMTA2fDN+NvO128kiHLfVzxCtUu1Z77hEzWv3qBbIeiu8bRVW//1Pq8H+/5P8L3YsUXqCVDAU6FHNOof2rB6JBTSTPmFmNAPdQBLTXoSA5zrM8h/u6iS3P7oxm8PRDSdBI4FjPPmyHZgzMQ8nSjuRs0wF+xwoEHpsRMMwgv8R3QAY3xn9bjBQSh3NOL8tyWqFt3/4OFQ9XowQ3vuaOIQF2AFrBAgogQTf6S+T1RVGcT87Ye9q87osMrNs2ng+51PJadMD4E5bZGo9nvv9dUx0HCvT3I+c6GgW741Ei1AU5+RYdVXTYpyzL+4D7iqKYTs5MPgTNsnkDJSov+96ZXh1V1f8sFBi6W9eryrK8AsByGFaW5UtFUQRPk4GlpQ5+bgeMKHVo+dW+f5DlAnko8JPW1TeRjX2/RdYHWsbBw1v+uQCuthzPR+c6XFEUxUHWdVzf2Y652WPuQd2O1gCLS+05fREqtMBbUZU6YPUGy/56ZLt/UxTFCOCcsizvK4qisI5As7g6+Nmgw7HHW9dhL+Ebx1iH91rO16JgB7L3N8hVPtF4j9+36maTdfEQua959d3z/fcYy/HmNXgahM566EzFf9DqgXuLorgPAf4VaJbEC6hQmYLsfWblmY8jew7bDpk/T8XeyFn3L6LC5lDqmNED+cCB/vsWnaJAtqp1vGhm2Uxgpm3wY8jug+e7kb+sBt61rQ4Aum3D7gKHJpO+/Ib/C59diez6d+ZprK/d7L8/j4rv8babs8uyvLwoitPJve/Hkbh8sOV8tO+fbRt4xr+rjvlR+9hElCystIwfJPdwXkzuj/wKOuj8Qfvca9sYZ/AethkYNwC4otThyDWfLYriaettF3Jrx6UVOe1XlQd5VljN9qyrZRWdhh+22mz4y+NlZesO63QECvyzLZ+LWt71S9SkOhDhRVv23w441e8OjHuKOrZH3An5Lm4ZW9WnbzSf91PHqFXGrq8iPKjaS7syDxZ+0LKcb9xZgWYlbrYN70DObK7GLwAqcXoF+kBxJhnL70IJVSvOxs8HogQtsPrdllhwtHma53GHvcS/Y6zb0CnA89bNhdbFqduQbSs+dELJ2SjqMXAB9Tgf1/Ugt8Nq6z0gf56C/GeK5dzNY6/60e1+xrC2nl3RxSg/M8a7wPI+tg053IjynjPRjN+ZLXHzFBRTJvv+26j7ybPIR1t1GTZd+DkHWIatPnCh5fgCdZwZW5HPr8qyvB7A4+tnv/iK3xV23tu/v968ztoGj4EzM4BXy7K8q2pXRVEMoZ5zXov86g0UV6YUWonzp1IHy1dzqZBtf+oxvzX+RD7yNtDTOcD5pC2voG4vo0ncneVndEC+1s0yjHcHZgVWz/ZYHqKec7bmKbMrMaQa7yMPuMPyDr/6oJwhrq363rOVnOB3LbgT8bwHWrWzADUN3rQuulP3x0HAvzoHq2LlWLRivoq7rXgQ/7aZ15G+2496jAwM/QNb+/Bc87oFA63T4WgF+yL/PuLsACr+Ucl/+1j//fhg223V4RyEG2up2yHIzqvYPNXjiNok9LjCvG0r3q5zHretXCvywmGo0bDCMlpFfhSLWiRi/n+jFRt7md9HfM1XkO3eCHS13ZxP1j+/a/HVrlT8iVx5UK2rfoWa7zWsI/O1C6nnVqv8rD1pO1d+3Hnb91p4ixgZuXmMpaqToBvRDOHIcy+r5FTLyG2/+lPHyXf8zOAp/GwG9RzrGer1YtVvRqB6LPLIaq4PW8fIj1nWI6nnhq31QYxz7TbuD19urTMHs3WeuwbhQi+U/4TtvUE9J3gH2Vhrrt2R9OEt95mXV8lVCh2o13RHkDb7RMs4ImZF7G8zny+K4jH/bRfyQ2oVN2b456WWx0bq9fZYKrmI8eRg9BGvWoNWa4iwxdOp+2TYR+gsYgHUsWsYGY+qsfYm5KdLybPRZrf4T9Tc/+ZxfJR6LrCCeh58DLCsLMv/LIri6wjrtvg2bIn3Ic911P2wWpNFHTCdrWP3r1GMmwmc0FI/B852t5yh7bq5FWdfaal5o6YHYV9g3JZxkvl5R8vnDMs0Yl58TA2dbIvCLy+i7Vg4omWsR7J1byP6JzcgW4t/x1L35cDCV6n4CbmlYcg5YsYw4IHIj4Lhst6nC13NpB5ne1Lvzw1rselhLTJvreWCh4iV0b8L3UQNWNXVNF8bMp/aIqduzo1a6+Pl1ONv5BDx7ukoB72begzYbP1dZ5t/379rq1YF4ehyKr5J4sg+1Gvy1hzzy5XnVHOv+WX256o1XlU3kRuEr/amjk2Bw0u2ka/29Phvpe068VQqdYV1Hj3Tai3/HFkPhZxmUq+5av0Dy7atXCrkGfnxWOq5VOhunMcWfauxOKcksS5sMzBqI230tf3OKr7GuO8h4yzU88LrEM5U+37jiuwRxvgjJ2itQcMmT6Dt2B55/g0tunzFfEesCHsKvBrZBi8h09ZYHzEy/r6ceg76osfbiqsvsHWtf3lFzkGR0/4F8JjxZnypj2iRn4d/RP9kEvW6eRT1HLs1341edOsY/oL80PMfKEdr7eOGzO+kXqMeQwVXK3naauPMnJbatlUXC9i6t1OliA0Rg0F6/wXZn/uvFttt0yex75J5297++3jq+QtIp8taegdR87f6VbUeurGFl/8n+rOstGqooYYaaqihhhpqqKGGGmqooYYaaqihhhpqqKGGGmqooQ+idh82Aw011FBDDTXUUEMNNdRQQw011FBDDTXUUEMNNdRQQw011Hy0aqihhhpqqKGGGmqooYYaaqihhhpqqKGGGmqooYYaauhDp+ajVUMNNdRQQw011FBDDTXUUEMNNdRQQw011FBDDTXUUEMfOjUfrRpqqKGGGmqooYYaaqihhhpqqKGGGmqooYYaaqihhhr60Kn5aNVQQw011FBDDTXUUEMNNdRQQw011FBDDTXUUEMNNdTQh07/B5HDyB9xsn0tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 12))\n",
    "dendrogram(Z_df_average)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "Z_df_ward = linkage(X_standard, 'ward', metric='euclidean')\n",
    "Z_df_ward.shape\n",
    "plt.figure(figsize=(30, 12))\n",
    "dendrogram(Z_df_ward)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "Z_df_complete = linkage(X_standard, 'complete', metric='euclidean')\n",
    "Z_df_complete.shape\n",
    "plt.figure(figsize=(30, 12))\n",
    "dendrogram(Z_df_complete)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recreate the dendrogram for last 10 merged clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hint: Use truncate_mode='lastp' attribute in dendrogram function to arrive at dendrogram\n",
    "dendrogram(\n",
    "    Z_df_complete,\n",
    "    truncate_mode='lastp',  # show only the last p merged clusters\n",
    "    p=10,  # show only the last p merged clusters\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_distance = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "hier_clusters = fcluster(Z_df_complete, max_distance, criterion='distance')\n",
    "hier_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(hier_clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### plt.figure(figsize=(10, 8))\n",
    "plt.scatter(X_standard[:,0], X_standard[:,1], c=hier_clusters)  # plot points with cluster dependent colors\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.preprocessing import normalize \n",
    "from sklearn.neighbors import NearestNeighbors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_default = DBSCAN(eps = 0.0375, min_samples = 6).fit(X_standard) \n",
    "labels = db_default.labels_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = NearestNeighbors(n_neighbors=2)\n",
    "nbrs = neigh.fit(X_standard)\n",
    "distances, indices = nbrs.kneighbors(X_standard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12a86ce80>]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distances = np.sort(distances, axis=0)\n",
    "distances = distances[:,1]\n",
    "plt.plot(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(algorithm='auto', eps=2.0, leaf_size=30, metric='euclidean',\n",
       "    metric_params=None, min_samples=10, n_jobs=None, p=None)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_default = DBSCAN(eps=1.5, min_samples=10)\n",
    "db_default.fit(X_standard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, -1]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = db_default.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, -1]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['blue', 'red', 'orange', 'green', 'purple', 'black', 'brown', 'cyan', 'yellow', 'pink']\n",
    "vectorizer = np.vectorize(lambda x: colors[x % len(colors)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12a58eef0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_standard[:,0], X_standard[:,1], c=vectorizer(clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
